The growing demand for high data rates requires communication systems with great analog bandwidths and high spectral efficiency. In [1], [2], a multicarrier enabled baseband subsampling multiple input multiple output system (MBSMIMO) has been proposed for wideband applications of the Discrete Multitone (DMT) modulation, the baseband version of OFDM. MBS-MIMO divides the DMT signal into subbands, each one sampled at a fraction of the Nyquist rate. Unlike competing methods like time-interleaved sampling (TI) or Hybrid Filter Bank (HFB) converters, MBS-MIMO does neither require equally spaced sampling, nor equal gains in the subbands, nor high-order subband filters. It has a wider dynamic range than TI converters and extends the bandwidth of the employed converter technology by the number of subbands chosen. This present paper demonstrates that MBS-MIMO can be used for carrier-frequency transmission, too, using an IQ modulator and demodulator. It turns out that, without any further measures, the MBS-MIMO receiver has the ability to compensate for frequency dependent IQ modulator gain and phase imbalance. An IQ modulated MBS-MIMO system at 3,5 GHz with adjustable IQ imbalance has been set up to prove the concept

A Smart Grid consists of various smart devices communicating with each other in real time and thus is a vulnerable system prone to many types of cyber-attacks. It is a crucial task to identify and prevent these types of attacks in order to avoid electrical power and capital loss. In this paper a logical scheme is proposed to tackle common cyber-attacks on smart grid. The hierarchy of the proposed system consists of three RTU's, a substation and a control centre via two way communication links in real time scenario. Python programming tool is used to model the proposed system. Socket programming is used to establish bidirectional data flow. In the proposed model server 1 is used to communicate between RTUs and substation, while server 2 is used for exchanging data between substation and control center. All the critical information of smart grid like voltage, frequency and voltage angle are encrypted through MD5 hash algorithm and later on decrypted at substation and Control Centre using proposed key authentication method.

Antenna with ultra wideband (UWB) characteristic is an important part in respiration radar system. The antenna properties have a contribution in improving the detection capability of the UWB respiration radar. In this paper, the Vivaldi antenna was proposed as UWB antenna for respiration radar with frequency range from 3 to 6 GHz. The simulation and measurement have been performed to evaluate its characteristics and its performance in supporting respiration radar operation. The proposed Vivaldi antenna has printed on using FR-4 substrate with relative permittivity of 4.3 and the thickness of 1.6 mm. The simulation and measurement show that the proposed antenna has an UWB characteristic that required by the respiration radar. The proposed antenna has an impedance bandwidth from 2.7 GHz to 6 GHz. With respect to the minimum distortion contribution, the transmission coefficient from transmitting to receiving antenna shows that the antenna has linear phase response. The directional radiation patterns of the antenna are also suitable for the radar system. The experiment results in respiration radar model show that the proposed antenna can be used to identify the reflected signal from chest wall at inhale and exhale a phase of respiration activity. The reflected signals from two different breathing volume have also been well-identified.

Nanosatellite are one of several satellite classes that have a size that generally refers to the standardization of cubesat which is 10×10×10 cm3. Nowadays nanosatellite are widely developed for various purposes such as air traffic monitoring with Automatic Dependent Surveillance-Broadcast (ADS-B) receiver as the mission. Telkom University and the nanosatellite Laboratory are researching a nanosatellite called Tel-U SAT. The nanosatellite uses a frequency of 1090 MHz which functions as ADS-B receiver, and air traffic control as primary mission. In this paper, presented a microstrip antenna design for nanosatellite with mission as ADS-B signals receiver. Therefore the antenna has voltage standing wave ratio (VSWR) = 1,9 in frequency of 1090 MHz. Then have unidirectional as the radiation pattern and right hand circular polarization (RHCP) as the polarization, gain 1,02 dB, 52 MHz bandwidth in working frequency of 1068 - 1120 MHz

Electronic mail or email is among the most popular data or message carrier. One of its powerful features is the sent message history records which have a long lifetime without big memory devices. However, with those technology advancements, the security aspects have become a serious concern. The ease of access to the networks has made an exposed leakage for some irresponsible parties who have the competencies to steal the information while the delivery streams take place. The email user is advised to add another security act such as encrypting toward the email contents before it being sent using ESP service. In this paper AES and 3DES cryptography method successfully implemented to securing email text messages. Email message being encrypted first using both algorithms, and then being evaluated and analyzed from various aspects. Evaluation's results shows that AES is better in terms of compile time, while 3DES is better in terms of increasing message's size after the encryption process, but the change in the addition of bytes is not significant. So based on the results of tests, email users are recommended to use AES encryption.

The use of online transportation in Indonesia is growing, people feel a lot of positive benefits felt, together with the development of online transportation, the development of financial technology as a means of payment for online transportation makes passengers easier to transact. However, the development of financial technology (FinTech) is still in the development stage or not yet stable where there are still problems with transactions using financial technology such as Top Up balances, failure to pay. Therefore, this study aims to find out how security concerns passengers of usage financial technology for online transportation transaction? The research using quantitative method by using SPSS with correlation bi-variate function to see the relationship of passenger security concerns factor. The research found several significant correlations factors from 202 respondents. The factors found are very important for development of FinTech in the future.

This research proposes a combination of dual protection on text messages transmission using Chinese Remainder Theorem (CRT) steganography and Rivest Cipher 4 (RC4) encrypting method. This combination aims to optimize the performance of encryption and message insertion into an image. Security This message is done by encrypting text messages using RC4 first, then the results are embedded in the grayscale type container image with the CRT method. The evaluation standards that will be used in this research are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), and Character Error Rate (CER). MSE, PSNR and SSIM are used as a measure of the quality of stego images. To determine the performance of the proposed method, message insertion is carried out in three types of sizes, namely maximum payload, half payload and one quarter payload. While the CER is used to find out the results of decryption of text messages. The resulting CER value is 0, this indicates the message was extracted and decrypted perfectly

Recommendation system requires supporting data that sometimes are heterogeneous either from its sources or from its data format. In the context of tourism recommendation system, a previous research resulted in the concept of collecting the Tourism Contextual Information (TCI) data to determine the relevant information as the support of tourism recommendation system. However, what things and how the data were collected have not been explained yet. By adopting the global Extract Transform Load (ETL) process, this research aims to show how the supporting data including the database structure, the data architecture, and the data representation in the tourism recommendation system are collected. The result of the research can be used for the need of further research - particularly on the tourism recommendation system considering the change of weather or traffic condition and its effect on making decision when planning a travel or a tour.

In the age of the rapid growth of Internet technology, people are connecting to the network continuously. Request is made in seconds, resulting in congestion in the data center network. This makes the researcher tried to make a breakthrough in order to reduce the network latency. One of many proposals is the use of the active queue management (AQM). Active queue management focuses on dropping the network packets inside a buffer to reduce network congestion. This method is able to drop packets based on probabilistic manner. In this paper, we evaluate different AQM algorithms to the Data Center Network (DCN) using fat tree architecture. We conducted some experiments using NS-3 to evaluate the network performance of different AQM algorithms. The simulation is done using a research based tool for computer network design, NS- software on Ubuntu Operating Systems. NetAnim, the visualization and monitoring tool for network is used for the evaluation. Throughput, packet drop, and delay are measured and compared. Best throughput is gained when ARED AQM is used which is 498.5 kpbs. It is 37 kbps higher than NLRED and 59 kbps higher than those of RED. In terms of packet drop, the ARED shows the lowest packet drop of 2230 packet drop. ARED also has the lowest delay, 0.132 second, which is slightly different from NLRED with 0.133 second delay, whereas RED's delay is 0.177 second.

Indonesia is the largest archipelagic country in the world consisting of 17,499 islands from Sabang to Merauke. With the vastness of the area, Indonesia has so many and scattered tourist attractions. Indonesia is also a target of tourism by local and foreign tourists. Problems that occur are many tourist attractions that can not be known by tourists so that they experience a very serious problem in getting information related to the culinary. One of the problems of information about its existence which is spread in several locations and knowledge about the area that is not good, especially for tourists from outside the city. Of course, if this is ignored continuously will have a negative impact on culinary tourism, such as the lonely tourists who come to visit in the country of Indonesia. This study provides a solution that is seen from the existing problems, it is necessary to answer the need for fast and more efficient information about culinary attractions in Indonesia, namely by developing an Android-based GIS-based application that provides an easy search for information and culinary tourism locations. user required. Software development uses the waterfall method and tests on users with questionnaires to users. The results of this study, culinary tourism mobile applications that are based on the results of tests carried out obtain a percentage score of 81-100, which can be interpreted that this application is very feasible to use and apply to provide culinary information.

The research objective was to develop a learning media support system using chatbot technology that was integrated with the LINE messaging mobile application and content management system (CMS) with homeschooling students and teachers as target users. The renewal of this research is to add access to subject matter features and discussion forum features. These features do not yet exist in the chatbot application system in other studies. Authentication on the chatbot is needed to find out who the user is accessing the chatbot for the material, discussion forum, and work on the problem training and National Examination exercises. E-learning innovation from chatbots is integrated with the LINE and CMS messaging mobile application that contains subject matter, exam practice, discussion forums, and evaluation of student evaluations. This system supports students who have limited time to attend traditional face-to-face classes and have limited access to laptops. The results of this study prove that Chatbot on smartphones can maximize student learning functions, increase student interest and achievement (value).

Tourists intending to go on vacation are always searching for information related to the places they will visit by website or travel applications. The more and later information obtained, the better the preparations made by tourists on their tour enabling them to have a good travel experience then. A good tour preparation, however, needs to be supported by the latest information as occurred in tourist sites such as traffic conditions, weather conditions and the crowd of tourist attractions. The latest information about tourism objects needs to be facilitated with the information notification about what occurs in the tourist sites to be visited. In this paper, we present the results of research on how the prototype of a tourism planning system with a two-way relation that can make it easier for tourists to provide and obtain information from any various sources in regard to the attractions they will visit. The High Fidelity Prototype of tourism planning system with a two-way relation was made using the Adobe XD with the User Centered Design method and was evaluated using Usability Testing.

Least Significant Bit replacement, a spatial domain algorithm, is the most popular and widely used technique in image steganography due to its simplicity and effectiveness. Different methods of data hiding in spatial domain have been proposed and continue to be improved upon. Among them, the LSB replacement method is quite simple and the most popular. However, because the LSB replacement method is quite simple, compared to the other methods, some of its security issues must be improved upon. This paper proposes a highly secured data hiding technique in the spatial domain of image steganography. The proposed scheme takes the message bit and performs XOR operation with the 7th bit of every RGB component and, after then, the produced output is embedded within the 8th bit of each component of RGB. The embedding procedure is done in a way that there will be no sign of original message inside the cover object and, obviously, without using any outside key. A detailed study of the proposed LSB replacement algorithm including PSNR- and MSE-based investigations has been made. Experimental results shows a very good peak signal-to-noise ratio (PSNR) (55.90 dB for 65,536 bits of message within a 256x256 pixel cover image) and mean square error (MSE) value which indicates to less imperceptibility and more security. The comparative results prove that the proposed technique provides more security to secret information sharing, compared to other related techniques.

Random numbers are important tools for generating secret keys, encrypting messages, or masking the content of certain protocols with a random sequence that can be deterministically generated. The lack of assurance about the random numbers generated can cause serious damage to cryptographic protocols, prompting vulnerabilities to be exploited by the attackers. In this paper, a new pseudo - random number generator algorithm that uses dynamic system clock converted to Epoch Timestamp as PRNG seed was developed. The algorithm uses a Linear Congruential Generator (LCG) algorithm that produces a sequence of pseudo - randomized numbers that performs mathematical operations to transform numbers that appears to be unrelated to the Seed. Simulation result shows that the new PRNG algorithm does not generate repeated random numbers based on the frequency of iteration, a good indicator that the key for random numbers is secured. Numerical analysis using NIST Test Suite results concerning to random sequences generated random numbers has a total average of 0.342 P-value. For a p-value ≥ 0.001, a sequence would be considered to be random with a confidence of 99.9%. This shows that robustness and unpredictability were achieved. Hence, It is highly deterministic in nature and has a good quality of Pseudo-Random Numbers. It is therefore a good source of a session key generation for encryption, reciprocal in the authentication schemes and other cryptographic algorithm parameters that improve and secure data from any type of security attack.

The Tiny Encryption Algorithm (TEA) has been proven to be suitable for constrained devices because of its lightness. However, TEA suffers from equivalent key and related key attacks. This paper proposed a modification to TEA to improve its security by rotating its subkeys to every round function as the new key scheduling scheme. Also, the round function of TEA was also modified by shifting the keys before integrating to the functions. The modified TEA was benchmark with the original TEA with avalanche effect and completeness test. The modified TEA avalanche effect is 54.4% against 47.2% for TEA, and its' completeness test is 51.4% against 49% for TEA. The modified TEA was also tested with equivalent keys and produces different ciphertext. The tests showed that the modified TEA is more secure than the original TEA.

This research contributes optimal dispersion compensating by adding a new scheme of parallel compensation with Dispersion Compensating Fiber (DCF). Using four wavelengths around 1550 nm, this research shows that scheme without DCF resulting in a very large dispersion with Q factor value 6 at 150 km, post-compensation scheme produces a Q factor with a value of 6.6 at a distance about 400 km and the pre-compensation scheme produces a Q factor with a value of 7.4 at a distance of about 600 km. Merging post and pre-compensation in serial cable made length performance further than single scheme up to 50%. However, performance increase significantly is obtained with new parallel scheme around 85%.

Many people use the internet in their daily communication. But the risks of data theft on the internet are quite high, so sending security is a very important thing for data. Cryptography and steganography is a technique used to secure data to minimize data theft and access by unauthorized people. Combination of Least Significant Bit (LSB) - Advanced Encryption Standard (AES) - Base64 is proposed in this study to provide protection for messages and various file formats embedded in digital images. Secret messages are encrypted with the AES and Base64 methods before being inserted into the image using the LSB method. The study also analyzed the performance of the LSB-AES-Base64 combination of algorithms on various files and the size of the cover image. Cover images used image with RGB channels. For measurement of imperceptibility performance used Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and histogram analysis. Based on the results of testing, the proposed method can work well with the value of PNSR and MSE in a very good category. The stego image histogram is also identic to the original image.

Malaria is a vector-borne disease that is spread throughout all regions, especially in the tropical countries. Early malaria detection is necessary to reduce the mortality rate. Microscopic imaging technique is still considered as gold standard for diagnosing malaria. However, due to numerous number of patients with limited parasitological experts, it is necessary to have such an automated system for diagnosing malaria. To obtain more accurate diagnosis, it is essential to have an accurate segmentation of Plasmodium. This study aims to develop a scheme to segment Plasmodium in digital thin blood smear images with different characteristics. There are three main steps consisting of preprocessing, grouping of image characteristics and segmenting Plasmodium. Saturation channel is extracted from HSV in the preprocessing. Difference of image standard deviation is keys for grouping the image characteristics. There are three groups of image that is segmented in different ways. Otsu thresholding combined with morphological image processing is used to segment Plasmodium. The data used contains 124 cropped images. The evaluation results achieve 97.99%, 82.23% and 99.33% of accuracy, sensitivity and specificity, respectively. These high evaluation values indicate that the proposed method is suitable to support the development of CAD in malaria diagnosing.

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium Tuberculosis (MTB). Chest X-ray (CXR) image has been the main tool for detecting lung TB historically. Chest X-Ray (CXR) images are analyzed by radiologists to determine whether or not there are signs of TB in the lungs. The results of the analysis by radiologists in analyzing CXR images are influenced by the subjectivity of radiologists, such as experience from radiologists, conditions of observation, fatigue, and others. The subjectivity factor of the radiologist can be overcome by the computer aided diagnosis system. This paper proposed a TB detection system on CXR images using optimized Gray Level Co-Occurrence Matrix (GLCM) features as the input. GLCM is optimized using the Principal Component Analysis (PCA) and then classified using the Support Vector Machine (SVM). In this paper, CXR images were classified as normal, primary TB (PTB) and secondary TB (STB). The results of this paper indicate that the classification system with optimized GLCM as input has better performance than the classification system with regular GLCM as input.

The Intelligent Transportation System includes management of vehicle transportation. Video from surveillance cameras can be used for monitoring the number of vehicles and speed using digital image processing. However, in rainy conditions the monitoring process will be less accurate. So, removing rain noise is a solution that can be used. In this paper, we will present the rain removal method using low frequency part and high frequency part from video frames. For each frame, low frequency as an image non-rain. Then, high frequency part as input from the guided filter, so we get a non-rain component from the high frequency part, and add a low frequency part is the restored frames. we also restore the nature of the background edge. The experiment will generate rain noise from videos without rain. The type of rain is heavy rain, moderate rain and light rain. The result of rain removal will be calculated image quality with a peak signal to noise ratio (PSNR). In addition, proper selection of radius and regulation parameters will reduce the rain effect more optimally. To be more detailed, we also test the performance of method on videos with static and moving foreground. The application of guided filter algorithm for three times, contained in preprocessing, filtering and recovering, can improve image quality.

The diagnosis of hypertensive retinopathy can be done using analysis of the retinal fundus image. The initial analysis that can be done is about the curvature of the blood tortuosity. The analysis was carried out by segmenting existing blood vessels. Segmentation can be done using the clustering algorithm, one of which is the fuzzy c-means (FCM) algorithm. This study aims to analyze the performance of the FCM algorithm with a variable number of cluster functions. The method used is divided into three stages, namely preprocessing, segmentation and performance analysis, Preprocessing consists of channel separation, CLAHE, and median filtering processes. The segmentation process consists of 2D to 1D conversion, clustering, thresholding and masking processes. The last process is to perform a performance analysis to compare with manual segmentation. Parameter performance used is sensitivity, specificity and are under the curve (AUC). The test results show that the number of clusters 3 and 15 uses the median threshold method. The results show that the number of clusters and the threshold determination method affects the results of segmentation.

Wood is one of the raw materials that is important for helping human needs such as home appliance like chair, table, and the other things made from wood. Wood industries require wood for produce their product that made from wood. There are several classes of wood must be classified before entering the process stage in wood industries. The class of wood is indicated the quality of the wood itself. So, wood industries must classify carefully for increase the productivity. Wood industries usually classify the wood with manual method such as classifying with eyesight of human however it produces about 55% of correct classification rate. Convolutional Neural Network (CNN) is a development from Artificial Neural Network (ANN) to classify the image, image segmentation, and object recognition with high accuracy and high performance. Wood classification is one of the texture classifications because the wood can be classified depend on texture of the wood fiber itself. So, Convolutional Neural Network (CNN) comes to solve this problem. Deep Convolutional Neural Network (D-CNN) is developed to improve the accuracy and performance from many deep layers of Convolutional Neural Network (CNN) model and is designed for classify image with small dataset. This paper investigates the usage of Deep Convolutional Neural Network (D-CNN) with transfer learning method and bottleneck features for wood classification with small dataset. There are five different classes of wood classifications in this paper such as class I, class II, class III, class IV, and class V. The best quality of this wood is class I and the worst quality in this wood is class V. Transfer learning method and bottleneck features can increase the accuracy of Deep Convolutional Neural Network (D-CNN). In this paper, we achieve 95.69% of accuracy with transfer learning method and bottleneck features.

Technological developments in the social field have brought about changes in terms of social communication, mindset, and changing attitudes, especially for elementary and middle school age children. In addition, technological developments in the social field also influence the ethical aspects of communication, where many young people give information excessively on social media which includes family and romance. One of the negative effects of social change caused by technological developments is the widespread circulation of content that contains pornographic elements. The pornographic content is generally accessed from social media such as Facebook, Instagram, Path, Whatsapp and Youtube to access and view pornographic content. The purpose of this study was to develop a system for identifying pornographic image negative content by designing a prototype of pornographic image detection using the YCbCr method and color space (RGB) on computer vision by filtering the algorithm of previous researchers, measuring instruments to determine the accuracy of prototypes using the Confusion Matrix algorithm. The process of image detection test produces an accuracy rate of 76%, precision value = 82.142%, and recall value = 76.666% of 50 random datasets between pornographic positive images and non-pornographic images for the YCbCr method and accuracy rate = 43.42%, precision value = 44.23%, and recall value = 50.17% of 146 random datasets between pornographic positive images, non-pornographic images and semi-pornographic images for the RGB method

Interference coordination technique using Fractional Frequency Reuse (FFR) is compatible to Orthogonal Frequency Division Multiple Access (OFDMA) based wireless networks which expected to be orthogonal to each other, as well inter-cell interference (ICI) is the main source of interference that restrictive for users near the boundary cells. This research is focusing on reduction of interference in inter-cell at the same time reduces the Power in (Watt), Capacity and Base Station Power in (Watt) thus improves the performance of system. The implementation system models of Fractional Frequency Reuse (FFR), clarifies that interference problem in inter-cell can be resolve through MATLAB simulation via formulating the optimization problem. At the end of stage, simulation results demonstrate that the proposed scheme FFR outperforms conventional Reuse-1 and Reuse-1/2 schemes by develop 12.98% in terms of Bit Error Rate (BER) and Signal to Interference Ratio (SINR).

Bandwidth capacity and high data rates improvement in Eastern Indonesia become an important aspects in empowering communities that are aimed at facilitating access to information in various sectors. The East Palapa Ring Package project was launched by the Ministry of Communication and Information to meet these needs. The design of optical fiber networks in East Nusa Tenggara Province was carried out in this study to support that project. This province is one of the areas that has a high data rates needs, especially for the security and tourism sector because its territory is directly adjacent to other countries and has a high potential of tourism there. A total of 22 routes connecting districts/cities in East Nusa Tenggara were proposed by using Power Link Budget and Rise Time Analysis. From the results, it is known that the Power Loss and Dispersion Time in the proposed network do not exceed the permitted limits, which are 53 dBm and 233.33 ps respectively. Therefore, the proposed optical fiber network is in accordance with the expected and feasible targets to be carried out.

Innovation in terms of the utilization of ICT (Information and Communication Technology) in social life is highly required to prompt the implementation of good social behavior - particularly among students in school. To do so, it requires a system innovation that is able to facilitate the process of goodness behavior implementation among students in school. The system must be capable of supporting an efficient information exchange among students. To achieve this, this research would use the two-way relationship as the base of data processing. In building the concept of two-way relationship, it requires the design of the structured data distribution flow concept in the form of software architecture. Blackboard architecture is an architectural type that would be used to design the information distribution concept among users in which the designed blackboard would be efficient with the independent components to support the data distribution and the implementation of the goodness behavior among students.

Keywords- Two-way relationship, Blackboard architecture, Goodness Behavior

In this paper, we have designed a compact microstrip patch antenna having parasitic patch and partial ground plane to achieve high bandwidth and resonating frequency along with minimum attenuation levels, the antenna is working on multiple bands of 13-16 GHz and 17-20 GHz that lies in the Ku and K bands and can be used for satellite applications, minimum attenuation levels are achieved at the resonant frequencies of 14.6 GHz and 19.2 GHz of -58 dB and -41 dB respectively. The antenna is showing a good impedance matching bandwidth with multiple bands.

Cancer is viewed as the most harmful disease in the world. One of the most deadly cancer diseases attacking the women is breast cancer. In this paper, Naïve Bayes with Kernel Density Estimation was used as it could enable the Naïve Bayes to process the quantitative data. However, the performance of KDE (kernel density estimation) highly depends upon the size of the bandwidth parameter used to control the curve. An accurate choice of parameter bandwidth (h) using trial and error technique requires a long process. The aim of this research is to compare the optimization of grid search parameter with the genetic algorithm to determine the bandwidth parameter (h) on the Naïve Bayes Kernel Density Estimator algorithm. The parameters used to compare the two optimization methods included accuracy, AUC (Area under Curve), and computation time. The experiment results on the test of the significance value on the comparison of grid search and algorithm in determination of the bandwidth parameter (h) naïve Bayes kernel density used Mann-Whitney Test. The result of the significance ranking showed that mean rank of the genetic algorithm accuracy was 489.95 better than the mean value of the rank of grid search accuracy at only 189.05, mean rank of AUC genetic algorithm was 491.82 better than mean rank of AUC grid search only at 187.18. The mean rank of computation time grid search was 243.37 faster than mean rank of computation time genetic algorithm at 435.63.

LBlock-s key schedule algorithm is a modified version of LBlock key schedule algorithm that has been claimed to have better diffusion properties. Using correlation test based on Niaz method we conclude that LBlock-s' key schedule algorithm produced high correlation keys. In other words, their randomness properties are not good enough. In this paper, we improved LBlock-s key schedule algorithm to enhance its randomness properties against high correlation keys. The result show that our improve algorithm has produced lower correlation keys than LBlock-s in subkeys level, bytes level as well as bit level. On the other hand, our comparation of execution time of both key schedule algorithm shows that even our improved key schedule algorithm is more complex than LBlock, but their execution time has not significant gap.

Ultra Wide Band (UWB) radars are widely studied to be implemented in the medical field for detecting vital signs in humans such as heartbeat and respiration. UWB antenna is important issue in UWB radar system since the UWB characteristic isn't only determine form impedance bandwidth perspective, therefore the suitable UWB antenna design is required for UWB respiration radar. In this paper, we propose a bow-tie antenna design for UWB respiration radar system with self-complementary structure. The simulation and measurement experiments were conducted to investigate the proposed antenna characteristic and its performance in supporting the radar system in respiration detection process. In the experiment, the Vector Network Analyzer (VNA) is used to modelled the UWB respiration radar. The antenna is realized using FR4 dielectric substrate with relative permittivity of 4.3 and thickness of 1.6 mm. The antenna was design to cover the UWB range from 4-10 GHz. Simulation and measurement results show that the proposed antenna has fulfill the bandwidth requirement. The proposed antenna has a bandwidth within range 2.8 GHz to 10 GHz. Due to the minimum distortion point of view, the S21 result from both simulation and measurement indicate that the antenna has linear phase response. The Respiration radar experiments using VNA shows that the reflected signal from chest wall at exhale and inhale can be identified well using the proposed antenna.

The application of the theory of control to service systems may be very uncommon. One of the applications of controlling the principle to service systems grow to be the focal point on studies by the control issues and challenges which might be essential to service systems. Preferably, the customers want their service to be set up without delay. The ability of the company to respond quickly to the request depends on the ability of employees and the availability of the components necessary to provide the service. The focus of our current study is to develop useful insights for inventory management to prevent stock-outs and unfilled orders under different scenarios, exclusively for retailers in a telecommunication fulfillment process area. The contribution of this study occurs in the most appropriate modeling when faced with conditions in which supply in retail businesses in the telecommunications sector and customer demand is uncertain. Furthermore, we demonstrate how control unfulfilled order can be applied to service structures thru three case scenarios. The result shows that workforce availability and tools and product availability affect the quantity sold, where the magnitude of the customer orders will affect the magnitude of the unfulfilled order.

Borobudur is a temple where the building from andesite which is in an open space so that the temple stone will be susceptible to various problems that can cause stones to be damage and weathering. In this research, a system can be made that can study the types of damage to objects through the image file being tested, the segmentation method used K-means clustering, the method of texture feature extraction is Gray Level Co-occurrence Matrix (GLCM) and the classification method used K-Nearest Neighbor (KNN). In this test used 70 types of rocks that have causes of damage (alveol, microorganisms, salting) which are divided into two data, training data and testing data with a composition of 44 training data and 26 testing data. Then the testing of the test image dataset was 26 images consisting of 8 alveol images, 11 microorganisms images, and 7 salting images. The highest level of accuracy was obtained at 57.69% using the GLCM degree parameter θ = 45o and k = 7.

EMG signal has information to identify the pattern of palm movement. The method of feature extraction was put into observed pattern classification. Many scientists used domain of time, frequency and frequency time. This paper used statistical feature as an alternative on feature extraction which aimed to recognize palm gesture pattern. It gained 95% accuracy by using SVM Method with Mean, Standard Deviation (SD), RMS (as three best attributes) to recognize the pattern of HC, HO, WF and WE by using 3 electrodes. The second alternative was using two features, the Mean and SD, with 91.9% accuracy.

This paper proposes the hardware architecture of an artificial neural network coprocessor that its structure can be programmable. The number of neurons in each layer of a feedforward network can be set by writing configuration registers inside which enhance the flexibility of the computation. The application of handwritten digit recognition from the MNIST database was performed to verify the performance of proposed architecture. The design was developed with Verilog HDL and implemented on the Xilinx Artix-7 XC7A35T FPGA. The experimental results revealed that the speed of back-propagation learning and validation process can be up to 47 times faster than a computation on ARM Cortex-A4 CPU, while the recognition rate is still the same.

Indonesia culture captivates people both domestically and internationally. One of the prominent cultures is Cirebon Mask. There are five types of Cirebon masks: Panji, Samba, Rumyang, Tumenggung, and Kelana. In this work, we present texture features using the first-order statistical feature with outlier detection using z-score while K-Nearest Neighbour acted as the classifier. From this experiment, our proposed methods increase the accuracy up to 10% compared to the basic method.

Three-dimensional (3D) modeling process continues to develop rapidly. In its implementation, there are several 3D models that require elevation information from the model to be made. In this study, an experiment was carried out in the form of a test of Digital Elevation Model (DEM) data analysis which made it possible to serve as the basis for making 3D plains. The DEM data has a color depth that contains an elevation value that is described in a gray or grayscale level. This information will be used to create plain 3D modeling with the help of 3D modeling software. The object of the DEM data used as a sample is Seram Island, Maluku. The results showed that the DEM data provided by Earth Explorer was feasible to be used as the basis for 3D modeling of terrain that had many fields. The data can be seen by knowing the pixel value of each image detail based on the gray level in testing using 1 to 8 bits. Each bit increment will give a different level of detail. The height of the DEM data used can also be analyzed by converting it to contour information. To get better modeling results, it is necessary to take the level of resolution of the DEM data processing into account.

Security is one of the most important things for human life at this time. Complex activities often cause homes to be left unattended. One of the actions taken by most people to guard their homes while traveling is to use CCTV (Closed Circuit Television) cameras. This conventional CCTV is less effective because the camera is only recording without analyzing objects. From the shortcomings, the camera is made so that it can monitor the activities of the changes in the movement of objects seen by the camera, in this case the object detected is human movement. The monitoring system using this camera can detect passing objects. This paper proposes the adaptive background subtraction method needed to adapt to frame changes. The background frame will always be updated against the previous background intensity inference. Then it will analyze the effectiveness of the method. The effectiveness of the method used is then evaluated by comparing the results of object extraction with ground truth. The best success rate in object detection from object detection method is measured by calculating recall precision and F-measure values. The experimental results show satisfactory performance from the proposed method.

This study presents a technique to recognize handwritten Lontara characters. Lontara character is a traditional character in Indonesia which is used mostly in the southern area of Sulawesi during the kingdom era. The work consists of two stages. First, character segmentation is carried out with a combination of contour feature and sliding window technique to form a character boundary and extract character segments in images. Second, a Convolutional Neural Network (CNN) is used to classify/recognize the segmented characters. The dataset contains 23 Lontara characters with five combinations of diacritics and one special character, that falls into 139 classes. The result of the conducted experiments shows that CNN provides good results on the data set - obtaining 96% of accuracy. Also, segmentation and recognition are combined and produce a promising result.

Current manufacturing trend of the data-driven process and delivery is moving fast to bring out the new generation into the manufacturing industry, but still presents gaps in power plant process. The computerization of process activities at workstation holds the flexibility in power generation along mass customization to reach the target of intelligent smart process system through the design principle of industry 4.0; a new industrial revolution which is data exchange model found in intelligent technologies, cyber-physical systems, and the internet of things, cloud and cognitive computing. This paper present and review a generic framework maturity models and challenge for intelligent power generation feasibility to maintain the process activities including smart process design, smart control, smart monitoring, and smart machines. The similarities and current differences in power plant operation within this framework will be analyzed through the strategic plans of design principles as structured in previous research on Industry 4.0 assessment/maturity model and SPICE-based Industry 4.0-MM. The result is to create a common base guide understanding to implement power plant 4.0 to achieve the high process control, autonomous process benchmarking and process standardization.

Watermarking is a powerful technique to protect copyright from cyber-crime. This study proposes a random spread technique based on Discrete Cosine Transform (DCT) to improve image watermarking performance. The use of the random spread technique is proven to be able to improve the output quality of the watermarked image, where the watermark image is increasingly invisible to the eye after being pinned on the cover image. Better PSNR and MSE values prove this, besides watermark security also increases indirectly because embedding techniques spread randomly. To further improve security, the watermarking method is combined with the Beaufort encryption method.

This study proposes an encryption method in Arnold chaotic map based imagery. The uniqueness of the proposed method is to divide the image into smaller blocks, then each image is encrypted with the same chaotic map formula. Visually the results of image encryption are quite unique because they make the form of image encryption blocks that look different but actually have the same iteration. The chaotic map method is famous for its resistance to differential attacks. Therefore, the results of encryption from the proposed method need to be measured for performance against differential attacks. To measure the performance of the proposed method of measurement using measuring devices and UACI NPCR. Based on the results of the test we found a significant increase in the assessment of the UACI value but there was a decrease in the NPCR value. These results can be used as preliminary research for the development of methods in order to increase the value of UACI and NPCR at once

Steganography is one method of concealing data, research on this topic always aims to improve image quality, message payload and security. The Inverted LSB method is one technique that can be used to optimize the quality of the steganography method. This study proposes a modified inverted LSB technique. In the previous study the inverted LSB method used a two-bit LSB pattern, on the second bit and the third bit LSB. Based on the calculation pattern the calculation of bit changes in each pattern will determine whether or not the inverted LSB process is performed. This research proposes the use of the LSB three-bit pattern, where the pattern is taken from the second, third and fourth bits of LSB. The aim is to minimize LSB changes in each pixel of the cover image. Based on the tests that have been conducted, it has been proven that the inverted LSB method using three LSB patterns produces better results than the two-bit LSB pattern. Eight of the ten cover images tested, showed the results of an increase in the quality of the stego image. Chaotic map-based message encryption methods are also applied and combined with the LSB inverted method proposed to increase message security.

Image watermarking is the most popular copyright protection technique. Discrete Cosine Transform (DCT) is a transformation method that is still widely used today in image watermarking research. In the grayscale cover image there is only one channel so that the watermark is directly inserted on the channel, while the color cover image generally uses red, green and blue (RGB) color channels. Some previous research did direct copyright insertion on one color channel based on hypothesis and color theory related to the sensitivity of changes in pixel values to the human visual system. So, this research aims to carry out watermarking technique analysis based on the non-blind DCT method on color cover images. The watermarked image was tested by measuring image quality and watermark resistance to attacks. Based on the results of the tests that have been conducted, it can be concluded that the red channel has the best quality based on the value of MSE and PSNR, while the green channel has the best SSIM value. While for the durability of the blue channel watermark has the best average resistance based on the correlation calculation of the results of watermark image extraction.

This research aims to build an automatic sorting system for strawberry ripeness into three categories: unripe, partially ripe, and ripe. Manual fruit sorting has many weaknesses and limitations. One big problem is human error in the sorting process. Therefore, the implementation of artificial intelligence becomes more effective and efficient. Fruit ripeness is identified based on color characteristic, which is the RGB value of the image. This system uses Hue, Saturation, Value (HSV) to segment color and Multi-Class Support Vector Machine (SVM) with Radial Basic Function (RBF) kernel function to classify the strawberries. The data was taken using the Logitech C920 web camera. The dataset is divided into training and testing data. Training data is a video that consists of 70 unripe strawberries, 70 partially ripe strawberries, and 70 ripe strawberries. Testing data is a video that consists of 30 unripe strawberries, 30 partially ripe strawberries, and 30 ripe strawberries. The result shows that the strawberry ripeness classification system using Multi-class SVM with RBF kernel function produces up to 85.64% accuracy, where the parameters are C = 7 and gamma (γ) = 10-2.

One common unimodal human-computer interface is via a formal and discrete behavior on physical or virtual button clicks. That method may not be convenient for some people with knowledge or physical limitations. The raise for more pervasive, subtly blending computers in daily tasks may also find the method to be unnatural and inconvenient. Combination of modalities, or multimodal interaction is suggested to have advantages over unimodal input. Synergic multiple input and output modal is more expressive and natural. Specifically, studies have suggested that a combination of speech and gesture is preferred as being more effective and natural than speech or gesture alone. There are many successful approaches in providing robust gesture-only and speech-only interfaces, however, under extreme environments, unimodal approaches have user experience issues. To address the issue, the authors seek to explore and conduct a preliminary study and evaluation of speech-hand gesture recognition using Leap Motion and Windows Speech Recognition API. A conceptual framework would be developed as a result of this study.

The statue temple of recognition pattern used to distinguish the direction the digital image (photos) in order facilitate tourist or user knows the direction of the capture of temple image. In its application used algorithms the detection of edge Canny and artificial neural network Backpropagation and the process of extracting by using the PCA. Canny algorithm applied to the process of image where image processing rgb converted into binary image. While backpropagation algorithms used to recognize patterns of processing that image. The direction of which were recognized by the system which is West, South, Southeast, East, Northeast, and North. The level of accuracy against 36 testing data to the specifications 24 the image of practicing and 12 test image. The level of accuracy testing of the image of trained 100 %, while the image of a test of 83,33 %.

Mobile-based applications that use the Client-Server system certainly require an Application Programming Interface (API) as an intermediary to communicate with each other. To provide security, you can use an encryption method that is implemented in Representational State Transfer (REST) API authentication. Message Digest 5 (MD5) and Secure Hashing Algorithm 1 (SHA1) encryption are algorithms that are often used in this case. This study aims to examine the performance of the two algorithms. The Wireshark application is used to retrieve authentication data. Authentication data is still encrypted, then tested by Brute Force Attack using Hashcat tools. In addition, each algorithm is measured in terms of the time needed for the REST API Authentication process, which uses the Postman application. Based on Brute Force Attack testing the SHA1 encryption algorithm has the advantage of being stronger but the time needed for encryption is slower, when compared to the MD5 algorithm. Even though it's more tethered, the difference in encryption time needed is only 37.1 ms, so that SHA1 is still considered relevant for implementing security systems and REST API authentication on a mobile application

The implementation of Neighbor Discovery Protocol (NDP) in OpenFlow network was handled specifically by three different methods including the learning switch, reactive proxy, and proactive mechanism. All of the specified approaches still raised problems either consuming the controller's resources or forcing manual adjustment by the network administrator. This paper combined both reactive and proactive manner by deploying an application called Hybrid NDP proxy (HNDPP). The controller stored all information from Neighbor Solicitation (NS) request for generating OFPT_FLOW_MOD message as the guideline for the OpenFlow switch to response and craft the Neighbor Advertisement (NA) based on the defined selector and treatment. The research's results showed the effectiveness of the HNDPP by reducing the number of NS requests that entering the controller up to 20 packets on average. In addition, the waiting time for receiving NA packet lessened approximately below 0.5 ms.

Named Data Networking (NDN) is a new network architecture that has been projected as the future of internet architecture. Unlike the traditional internet approach which currently relies on client-server communication models to communicate each other, NDN relies on data as an entity. Hence the users only need the content and applications based on data naming, as there is no IP addresses needed. NDN is different than TCP/IP technology as NDN signs the data with Digital Signature to secure each data authenticity. Regarding huge number of uses on IP-based network, and the minimum number of NDN-based network implementation, the NDN-IP gateway are needed to map and forward the data from IP-based network to NDN-based network, and vice versa. These gateways are called Custom-Router Gateway in this study. The Custom-Router Gateway requires a new mechanism in conducting Digital Signature so that authenticity the data can be verified when it passes through the NDN-IP Custom-Router Gateway. This study propose a method to process the Digital Signature for the packet flows from IP-based network through NDN-based network. Future studies are needed to determine the impact of Digital Signature processing on the performance in forwarding the data from IP-based to NDN-based network and vice versa.

Reducing the internet traffic and consequently providing efficient bandwidth utilization is one of the most important advantages of using Fog computing. This reduction in internet traffic is done by reducing the amount of the processed data in Fog servers, before transmission to Cloud for further processing and permanent storage. For this purpose, different techniques and methods could be applied on the processed data in Fog servers such as filtering, summarizing and compression. The existing problem is that currently fog servers do the data size reduction process statically, which means they apply the same techniques on different types and amount of data provided by different applications. This approach is not suitable as fog servers could not meet the requirements of different types of applications. For this purpose, we propose a dynamic data size reduction approach in Fog servers which focuses on "compression" for reducing the internet traffic. Using this approach enables the Fog servers to reduce the size of data in the most efficient way, based on application characteristics and requirements.

Wireless Body Sensor Network (WBSN) based healthcare applications are categorized as those delay-sensitive online services that could not tolerate delay because of its negative and dangerous consequences on people's lives. Hence, many researchers try to propose novel approaches in order to improve the Quality of Service in online healthcare systems. As the network architecture plays an important role in providing a suitable level of QoS for any kind of applications, we aim to evaluate the performance of two different paradigms with different architectures, namely Cloud and Fog computing on performance of a WBSN based healthcare application. Using Cloud architecture for processing and storage of provided data by WBSNs has been deeply discussed in the literature. As Cloud resources are only accessible through the Internet, any network failure or Internet disconnection causes a high response time which is not tolerable by healthcare applications. In this paper, we investigate the role of Fog computing on reducing the response time and Internet traffic for a WBSN based healthcare application in a real-world test-bed. The achieved results show that in comparison to Cloud, the Fog-based architecture reduces the response time and Internet traffic by 46% and 77% respectively.

Recently, Compressive sensing (CS) paradigm has been considered as an attractive sampling method comparing with Nyquist Sampling Theorem. This is because of its ability to have the same efficiency by using a smaller number of samples. Within the centralized Cognitive Radio (CR) network, it can be individually applied at each Secondary User (SU). This leads to reduce its collected samples into a small number of the measurements. In this paper, this reduction is recognized by using the semi-orthogonal Regular Parity Check (RPC) matrix as a measurement matrix of CS paradigm. By this way, the overall samples that are going to be processed at the Fusion Centre FC to detect the holes are also reduced. Such that, this detection is carried on by the utilization the l2- minimization as a CS paradigm recovery algorithm. The integration between the semi-orthogonal RPC matrix and the l2- minimization recovery algorithm helps for reducing the complexity of the l2- minimization recovery algorithm. The results show that the proposed method enhances the ability of the l2- minimization recovery algorithm to estimate the status of the sub-. This is done with high accuracies at different number of SUs for different Signal to Noise Ratio (SNR) and without any prior information about PUs' number

LTE- Advanced is the development of LTE. It can aggregate two or more component carries to get higher throughput and spectrum efficiency. This paper presents RF signal quality and the number of sites that is used in Soreang-Pasir Koja highway based on simulation by implementing non-contiguous carrier aggregation with two Component Carriers. 15 MHz aggregated bandwidth at 5th as a primary serving cell and 3rd band as secondary serving cell are used in the simulation. Simulation result shows the improvement of RF signal parameter such as RSRP, SINR and throughput. Then, the number of sites can be minimized by implementation of non-contiguous carrier aggregation.

Detecting the objects behind the wall using Through the wall radar (TWR) face several problems due to the wall effect and antenna that used in radar system. The wall effect may degrade the detection result and the reflected signal from the target is therefore difficult to distinguish. Multiple reflection, wall attenuation contribute a distortion to the transmitted signal. In order to overcome the previously mention problems, a compensation method due to the wall effect to improve the detection capability of TWR is proposed in this paper. The proposed method consists of two part. The first part is wall and antenna effect extraction and the second is deconvolution. In this research, the laboratory experiment is performed to investigate the performance of the proposed method. TWR radar is modelled using Vector Network Analyzer (VNA). The experiment results show that the proposed method successfully compensates the wall and antenna effect and the reflected signal from target that masking the effect can be identified well. The effect can be extracted from received signal during the measurement. It makes the proposed method more realistic to be implemented.

Indonesia is a country that is famous for its rich spices, the use of spices is very diverse, can be used for cooking, medicine and beauty, but modernization and the era of instantaneous as now causes many people who can't distinguish types of spices, especially for young people who often use chemical products and instant products for cooking, medicine, and beauty even though the benefits and efficacy of spices are very much, spices have many types and have the same shape and color this causes quite difficult to distinguish between one type with other types, even though the properties and taste of spices vary, therefore it is very important to choose the right type of spice according to needs, the use of computer technology can be used to facilitate and assist humans in classifying or identifying spices. In this study four types of spices were used, namely ginger, turmeric, kaempferia galangal and galangal, this study will test the accuracy of the K-Nearest Neighbor Algorithm and also look for the best visual features that can be used to classify spices, the features used are shape, color, and texture. After conducting 150 test trials, the results showed that the truth level reached 84% with K = 5 and the best visual feature used was texture. Based on the results of the test results, it can be seen that the K-Nearest Neighbor Algorithm can be used to classify spices with good accuracy, but can be increased again by combining with other algorithms or using another classification algorithm.

This research proposes the use of a classifier built using the artificial neural network (ANN) technique for determining teacher engagement based on the Indonesian Teacher Engagement Index (ITEI). ANN classifier will be built in the form of a website. This study uses the concept of artificial intelligence and Artificial neural networks. This research produces Website Workflow and The Classifier Process in Website. In the future, the website will become a portal that can be used to determine the level of engagement of a teacher

Bank as a company that has many customers who conduct transactions every day, of course, has data that is increased continuously. In this paper, customer transaction data has been predicted to find potential customers in deposit offer. Data mining approach has been performed to classify potential customers for marketing through telemarketing. This data analysis can be used as a consideration in determining marketing strategy decisions for marketing managers. 15,713 data with 13 class attributes and 1 target class were obtained from UCI Machine Learning repository. The data was divided into 70% training data and 30% test data. Feedforward method of Artificial Neural Network was used to classify customer data. Multilayer Perceptron Neural Network and Radial Basis Function Neural Network were used to obtain optimal classification results. The result of this classification predicted potential customers to subscribe deposits. The result of this study indicated that the Radial Base Function Neural Network method with 95.3% accuracy and 86.1% sensitivity was a better method compared to the Multilayer Perceptron Neural Network method with 88.0% accuracy and 67.4% sensitivity.

Employees are one of the important points of driving the company. With the existence of capable human resources, the company has competitive advantage compared to competitors. But, how to have strong human resources is not an easy matter. Companies often have difficulty managing existing employees, determining which employees are talented, and developing talents that employees have. Therefore, need to develop method that help companies manage employees. This research will propose a way to classify employees into 4-box talent management. Employees will classified based on two aspects, namely performance and potential. The classification will use the C4.5 method, K-nearest neighbors (KNN), Support Vector Machine (SVM), Radial Basis Function Network (RBFN). In classification, we use 18 criteria to calculate the performance and potential of the employee. The results of mapping the performance and potential of employees will divide employees into 4 labels, label 1 (needs development or too new to evaluate), label 2 (everyday solid contributor), label 3 (potential to be executive in 3+ years), and label 4 (needs to be an executive in 2 years). Research will compare the results of the four methods above to get the method with the best accuracy in the classification of employees. The results expected to help companies create the suitable employee development program for each employee. In addition, the results of the research can used to help the recruitment team look for prospective employees based on the criteria of talented employees. Keywords-4-box grid talent management, employee classification, C4.5, K-nearest neighbors (KNN), Support Vector Machine (SVM), Radial Basis Function Network (RBFN)

The development of software systems is hard process. The software engineers doing their best during the software construction levels to produce high quality systems. Quality measured from multi perspectives. External and internal perspectives determined by McCall's quality model. In the scope of our work, we concerned with the internal quality. The code level represents the internal quality of the software. By using object-oriented metrics, we can measure code quality. These metrics reflects a clear idea about the code under investigation. Therefore, we proposed to use them during the code test level of the project. If there are any outliers in the values of the selected metrics, we can revise them by applying the appropriate corrections at the code. The code has variant design problems called design smells or code smells. These problems affects directly on the future maintenance of the project. One of the widely spread problem is the Feature Envy method. This type of code design problems causes complicated maintenance problems. In order to make the maintenance process completed in optimum time we have to get rid of this code problem. Therefore, earlier detection is the right way to minimize the time needed for further development and maintenance. In this article, we combined the OO metrics with some data mining techniques to classify the code's methods into two types as Feature Envy or not. The paper applied the technique on an open source java project. In addition, evaluate the results with other selected detection tools with different techniques.

The high-demand and the high price of food raw materials such as meat are widely used by meat sellers in traditional markets to commit fraud in order to reap more profits. The fraud was in the form of mixing beef with pork. This has become a food safety issue that has been rampant in recent years in Indonesia. By using electronic nose which has been assembled using electrochemical sensors and air sensors in temperature, pressure and air humidity, it can detect the purity of beef or mixed beef.

Umrah is an Islamic ritual that holds great value, rewards and spirituality in Islam. Millions of Muslims perform Umrah annually. However, the utilization of technology is not mature in Umrah performance, and most available applications are less effective in guiding users thoroughly. This study , proposes an application that assist and guide pilgrims in each Umrah pillar, for it facilitates a self-directed Umrah and incorporates GPS Positioning and counting techniques to develop Umrah Electronic Guide (Umrah E-Guide). In addition, it uses a GPS, JSON protocol and getLocation function in order to adapt to the persuasive design guidelines to make it more acceptable by end users. This application will provide an easy interface for pilgrimages of different ages and cultural backgrounds.

One of the core news is the statement of the informants. The statement can be expressed in the form of a direct or indirect quotation. The quotation can be done by means of the extraction of quotation in the news texts. The quotation extraction process can be initiated through the introduction of quotation sentences followed by the introduction about the informants. The introduction of the indirect quotation sentence is done by using the machine learning approach with the Support Vector Machine algorithm, while the direct one is done using the regular expression approach. The process of informant introduction uses the Named Entity Recognition. The proposed quotation extraction process was able to result in the accuracy of 90,76%, precision of 91,22% and recall of 90,74%. The result of the quotation extraction process can be developed to analyze the news sentiments as the news as a media to deliver the information should have a neutral sentiment. The sentiment to the news can be analyzed from the statement by the informant.

Increasing and growing of destinations in the tourism sector cause excessive information loads. Due to overloaded information, it will be difficult for tourists to determine the appropriate destination. In recent years, recommendation system has been developed to overcome these problems. In this study, a tourist attraction recommendation system model was designed with context-awareness and two-way relationships to produce recommendations based on tourist preferences in real-time. The Analytical Hierarchy Process (AHP) algorithm is used in weight input from users in determining location recommendations. The algorithm makes an alternative sequence of decisions and selects the best alternative when making decisions by weighting certain criteria. The system considers contextual information (i.e. weather, time, traffic flow) and a two-way relationship that is feedback (i.e. rating, comment) from other users regarding a particular destination which will be notified to the active users in real time. Further, contextual information and feedback from other users will affect active user to change the list of recommendations that have been given or remain on the recommendations given by the system.

The advances of the wireless sensor network (WSN) has brought the development of many applications in various fields such as environment, military, traffic monitoring systems, etc. The WSN network consists of a sink node and a vast number of sensor nodes that are spatially distributed. Sensor nodes have limitations in computing and battery, so effective routing protocols are needed to extend network lifetime. One of the frequently used routing protocols in WSN is Shortest Geographical Routing (SGP). Shortest geographical routing approach employs the hop-by-hop concept by utilizing neighboring nodes to forward data to the sink node. This fact may produce unbalance network load especially to the sensor nodes around the sink node will be busier than other nodes, causing the battery node to run out faster. To address this issue, we propose the swing routing approach to balance the network load and maintain the packet delivery ratio. This development is expected to be able to carry out the routing function by selecting the right and balanced traffic at distributed sensor nodes in the network. These approach aims to increase the WSN network lifetime and packet delivery ratio, minimizing the number of dead nodes and energy consumptions. Robust experiments have been conducted to evaluate the proposed approach. The experiment results show that the proposed swing routing produces a more extended network lifetime, higher packet delivery ratios and lower number of dead nodes compared to the shortest geographical routing approach as the popular routing protocol applied in the WSN environments.

The characteristics of the environment in which a communication system is applied must be recognized first, it is the key to the design of a communication system. This paper aims to describe the characteristic of ambient noise as a result of measurement on a real tank towing. The time-domain analysis shows that the probability density function (pdf) of environmental noise approaches the Gaussian distribution with a mean of 6.1792 x 10-6 Volt, and a standard deviation of 0.0019. In the frequency domain, the analysis shows that the spectrum in the frequency range 4 kHz ~ 20 kHz is generally flat more likely to be white. Simulation results prove that OFDM-time reversal communication has superior performance compared to conventional OFDM in AWGN channel. The BER value reaches 0.005 when SNR is 20 dB.

The attack on government system has risen following electronic government implementation. One of the cases is an attack on Indonesian General Election's tabulation system. The impact of the offense is decreasing on government's public image. The attack on the system is using IP Spoofing to cover the digital track of the assailant. IP spoofing has become infamous for a technique to avoid trace from law enforcer agency. This paper will propose a method to uncover IP spoofing attack by combining a network forensic analysis tool and IP correlation technique.

Deficiency in identifying human emotional stages occurs in most of the existing contemporary Human-Computer Interactions (HCI) systems. There are vast areas of the human stages that can be identified. One of them is the stage when a human feels familiar. Electroencephalogram (EEG) signal can be used to detect human affective stage in familiarity category. This research classifies familiarity in EEG signal using data from DEAP: A Database for Emotion Analysis Using Physiological Signals. The signal feature was extracted using Hjorth Descriptor producing three parameters. The parameters then fed to the Multilayer Perceptron as the classifier. The best accuracy achieved was 92.85% using three features combination, with 2.132 seconds of computation time.

Emotions are one of the advantages given by God to human beings compared to other living creatures. Emotions have an important role in human life. Many studies have been conducted to recognize human emotions using physiological measurements, one of which is Electroencephalograph (EEG). However, the previous researches have not discussed the types of wavelet families that have the best performance and canals that are optimal in the introduction of human emotions. In this paper, the power features of several types of wavelet families namely Daubechies, symlets, and coiflets with the Correlation Feature Selection (CFS) method to select the best features of alpha, beta, gamma, and tetha frequencies. According to the results, coiflets are a method of the wavelet family that has the best accuracy value in emotional recognition. The use of the CFS feature selection can improve the accuracy of the results from 81% to 93%, and the five most dominant channels in the power features of alpha and gamma band are T8, T7, C5, CP5, and TP7. Hence, it can be concluded that the temporal of the left brain is more dominant in the recognition of human emotions.

The development of Brain-Computer Interface technology nowadays has spread out in a case of classifying emotions based on brain signal (EEG) in human. One of the emotion parameters being focused in this research is arousal with the range from low (uninterested) to high (excited). This study is applying Principal Component Analysis (PCA) as the feature extraction of the EEG signal. Then, statistical calculations are applied to reduce the dimensions of features. The support vector machine (SVM) algorithm was used for classification. The results of this preliminary study obtained the highest accuracy of 60%.

Biodiversity that exists in nature shows the overall variation between living things both from the smallest levels, namely genes, species and ecosystem. One animal with a fairly high level of variation, namely birds chirping. Chirping has an identifier for each type both of the color of the feather, body shape, shape of the beak, food, how to find food and the most obvious is the difference in the chirping of birds. The problem faced is the number of species of birds chirping that are almost similar to each other so the introduction of birds with sound becomes quite difficult. This makes the introduction of birds with sound requires a special technique. The techniques used are transform wavelets and neural networks. At the end of the study, obtained Wavelet Package Decomposition extraction with training data used as many as 500 data. There is 1 preprocessing done by resampling (downsampling). The most optimal number of neurons to be used in hidden layers is 12 neurons with 500 epochs, the highest accuracy is 80.99% with momentum 0.2, learning rate 0.2 and wavelet daubechies2 while the lowest accuracy is 70.40% with momentum 0.8, learning rate 0.8 and wavelet daubechies10

Cardiac arrhythmia, especially Myocardial ischemia is commonly a main life-threatening cause. The electrocardiogram (ECG) is the most effective physical signal utilized in the prognosis of heart disorders. Some heart function pathologies can be indicated from the ECG signal. Some research present that changing the ST - T complex is an important variable correlated to myocardial ischemia. Therefore, this paper represented our simple systems in ST-interval and T wave detection using time domain analysis. The data used in this study is real-time from AD8232 with three leads and compared with the European ST-T database from Physionet, which without noise. As the results, the quality of our system can correctly detect ST-segment and T wave with 80% of sensitivity.

This research creates a web-based user-friendly system that aims to detect hoax and non-hoax news of Indonesian language news links. The input data is in the form of links and archive sites from the Forum Anti Fitnah Hasut dan Hoax (FAFHH), using 100 news for training data and 20 news for test data that is processed by crawling and then processed in the pre-processing phase, namely tokenizing, stop word and stemming. Next is the Term Frequency-Inverse Document Frequency (TF-IDF) stage to provide weighting data which will be input data at the classification stage using the Support Vector Machine (SVM) Algorithm with a linear kernel to detect hoax and non-hoax news. The experimental results show that the system can classify well with an accuracy of 85%.

The existence of the development of science and technology very rapidly, resulting in employees have to change the system work in accordance with the demands that exist in today. In an increasingly complex modern life, human beings will tend to experience emotional when humans are less able to adapt the desire to the reality that exists, both the reality that is inside and outside him. All sorts of emotional forms are basically caused by a lack of human understanding of their own limitations. Emotional behavior toward employees occurs in PT. WXYZ especially Programmer staff. Programmer in working always with high pressure and heavy schedule. Programmers are always in touch with other employees and this is where misunderstandings often occur. Where if a programmer faces problems always with emotional, especially if the Programmer is in high pressure and other employees are trying to ask about the problem. In this research the application of data mining using decision tree method and J48 algorithm to know the programmer's emotional behavior in response to the problem. Data mining analysis process in this research using data application software mining WEKA 3.9.3. So expected with this research obtained classification of the emotional tendency of a programmer.

Knowledge discovery has become more and more essential in the digital era. The very first step of knowledge discovery is data acquisition that can be gather from all across the field and that make the data type is also various and arguably large. Observed data can be obtained by several method, such as censor record or by frequent observation. Each of analysis process consist several steps, one of them is preprocessing. Preprocessing is a step or phase to identify, selection, or problem handling of the data. Missing values handling is included in the preprocessing step. The purpose of this research is to find out which type of approach of missing values handling works better on this type of dataset. This research uses three approaches and been compared each other such as Mode Imputation, Decision tree, and Class Center based Missing Values Imputation. To perform a fair comparison among them, several scenarios of missing values occurrence has been made. Dataset scenarios for this study is actually artificially "deleted" to be able to measure the performance of the methods. From the evaluation process, Decision Tree method shows a consistency even on different missing point's amount. Numerically have a slightly lower that the other methods.

The development of the business world grows rapidly, notably in the retail business. The sustainable business model of retail enterprises is a continuous cycle that illustrates the value creation for all stakeholders so they can last longer. Sustainable business models are divided into; long, medium and short level sustainability. The order to find out the company sustainability is by several standards or indicators. These indicators are known as sustainability indicator. One of the indicators is environmental indicators. Nevertheless, the existing way to measure these indicators is based on the expert. This research determines the sustainability level automatically by way of the combination of similarity processes with fuzzy. The combination calculated the similarity of 9 environmental business processes with benchmarks that used the Tree Declarative Pattern Edit Distance (TPED) method and the Cosine-Tree Declarative Pattern (Cosine-TPD). TPED method is used for the structural similarities and the Cosine-TPD is used for the behavioral similarities. Furthermore, the score of the similarity results calculated by the Fuzzy Rule-Based Classification to get the classification of the retail enterprises which based on the level of sustainability. The results confirmed that the classification of environmental business processes used to evaluate.

Supplier selection is important for the company. The risks caused by suppliers will have a significant impact on the company's performance. To mitigate this, each company has different characteristics regarding suppliers. There have been many previous studies regarding criteria in supplier selection, in this study using criteria that has been previously studied and adjusted to the direction of the company's business objectives. These criteria will be weighted by Best Worst Method. As a sample in this study, used data and information from ABC companies engaged in mining in Indonesia. Supplier assessment will be done by comparing MOORA and COPRAS approaches. The MOORA method is chosen because the calculation step is simpler and the average final ranking value difference is quite large. This study also produced key vendor selection criteria for ABC companies.

Population growth has an impact on increasing living needs, including the need for building materials. The high demand for building materials will spur the increase in mining activities, especially in Kediri Regency as the largest Pasir Batu producing district in East Java Province. On the other hand this industry also creates environmental changes that threaten the sustainability of environmental functions and the socio-cultural life. This was compounded by the lack of careful consideration in determining the potential zone for the Pasir Batu mining exploration. This paper proposes to determine the potential zone of Batu Pasir mining in Kediri Regency based on the specified criteria. Criteria are based on the definition of Mining and Exploration in UU RI 4/2009. The criteria used are grouped into 3 categories. First, natural factors consist of morphology, lithology, hydrology, vegetation and wild nature. Second, man-made factors consist of distance from the main road, distance from settlement and population density. Third, aesthetic factors consist of natural elements and tourist locations. The research method used is the integration of Geographic Information System (GIS) with the calculation of Multi Criteria Decision Making (MCDM) using the AHP (Analytical Hierarchy Process) method, Multi-Objective Optimization by Ratio Analysis (MOORA) and Complex Proportional Assessment (COPRAS). GIS is used for data integration so that the determination of zoning is visual and easier to understand. The AHP method is used to determine the weighting of each criterion. The results of weighting the criteria serve as the basis for ranking alternative locations between the MOORA and COPRAS methods. The final result shows that the weight rank of two methods is slightly different, but the method of COPRAS is more stable and complex. The judgement is very influential in the selection of criteria and deliberate hierarchy to determine the significance of each criterion on the weighting of criteria.

A metabolic disorder characterized by an increase in blood glucose due to insulin resistance and reduced ability of β-pancreatic cells to secrete insulin is a symptom of diabetes mellitus. Mechanism of the rate of glucose and insulin in the blood can be explained by a minimum of glucose and insulin models that have been modified by the Gravitational Search Algorithm (GSA) model. At a minimum, the modified model was used to detect insulin resistance by estimating the optimal value that shows insulin sensitivity and glucose effectiveness obtained using GSA from the Intravenous Glucose Tolerance Test (IVGTT). Suitability of the modified mathematical model with IVGTT experimental data can be seen from the graph produced from Matlab using Ode-45 method. If value of determination coefficient is above 90%, then the simulation results have good compatibility with the experimental data.

Prediction system is one of the tools that can support business decision making. However, existing prediction systems often give high error rate and high computational complexity. In order to make an accurate prediction system while reducing computational complexity, we proposed works that combines multi-layered clustering model using k-means++ as a technique to model data, and feature extraction LASSO to predict variables. K-means++ is chosen as clustering method because of its simplicity and its solution through poor initialization, while LASSO is chosen as feature extraction because of its good accuracy and its linear nature that cause model complexity to be simpler than non-linear feature extraction. First, k-means++ is applied in each layer, then centroids from each layer is transferred into central processor. Next, set of centroids is then proceeded using feature extraction LASSO to get prediction from a variable. Simulation results show that the proposed prediction system has given a good prediction accuracy with MAPE is about 23.45% and has reduced computational complexity by 89.77%.

Bladder cancer is the 10th most prevalent cancer in the world because the symptoms are difficult to see. Studies in bladder cancer and cancer in general has been gravitating towards miRNA due to its role in cancer gene expression. Previous studies have found specific miRNAs that regulate certain genes in cancer patients, such as miRNA-556-3p that regulates the DAB2IP gene. However, most studies of miRNA-gene regulation in bladder cancer uses wet lab methods or is only a meta-analysis of other studies. This study aims to create a pipeline to predict miRNA-gene regulation based on negative correlation values in a specific population between male and female that could have different gene expressions with each other due to environmental factors. For this study, miRNA and gene expression dataset were obtained from The Cancer Genome Atlas (TCGA). Computational analysis to determine miRNA and gene expression was also done using MySQL and correlation analysis was finally performed between aberrant miRNA and gene using MATLAB.

A total of 2,000 and 700 miRNA-gene interactions were found in female and male, respectively, showing different gene expressions between sexes from the same cause. The correlation values all peaked at intermediate level (between -0.5 to -0.8) showing the significance of the interaction between the miRNA-gene pairs. However, 387 out of 2,000 and 116 out of 700 interactions were also found to be unvalidated, meaning no studies has been conducted between the specific miRNA-gene pair to prove their interaction. This study also provides suggestion for future studies to further investigate and validate the miRNA-gene pair using RNA Hybrid, Molecular Docking, or Molecular Dynamics.

Colorectal carcinoma or colon cancer (CRC) is the 3rd most common cancers and one of the most leading cause of death worldwide. Current advance in diagnosis and chemotherapy able to improve the survival rate of patients in early-stage of CRC, however the same treatment fails to achieve satisfactory effect in patient with advanced stage CRC. Study of microRNAs (miRNAs) currently being developed in order to improve the outcomes of cancer treatment. miRNAas are small non-coding RNAs which perform a critical role in regulation of gene expression and may act as tumor suppressors in various type of cancer including in CRC. The focus in this study is to identify various miRNAs and their target genes particularly in Caucasian population based on the expression data obtained from NIH GDC Portal using TCGA-Assembler. The data were filtered and differentially expressed miRNAs (DEM) and genes (DEG) were determined. Afterwards, correlation test were applied to expression data by using MATLAB. Statistical analysis of expression data identified potential miRNAs targeting cancer genes including hsa-mir-141, hsa-mir-200a and hsa-mir-7-1. Further validation analysis can be conducted using RNAhybrid. Suggestions for future study including comparing the miRNA and target genes from 2 different populations (races) or in general populations.

Unemployment is a term given to someone who has not got a job or is looking for a job. High unemployment can also affect the economy because it can cause poverty and other social problems. To increase labor force participation, job search media are needed that can provide job vacancy recommendations that match the profile of job seekers.

Extended Weighted Tree Algorithms Extended is one of the semantic search algorithms where existing metadata consists of trees, labeled nodes, labeled and weighted branches. The similarity calculation will be done by comparing two tree structures, namely the job advertisement tree with the job search tree carried out by job applicants. The algorithm of similarity of an extended weighted tree initially matches strings in the part of a leaf node. Thus, if the leaf node is the same then it will produce a value of 1, whereas if it is different it produces a value of 0.

This research produces an accuracy rate of 88%, a precision level of 84.30% and a recall of 93.40%. Search results are compared with the results of manual calculations by adjusting the job title displayed by the system with the results of job openings obtained from manual calculations.

Red chili is an important type of spice in Indonesia. Based on the Ministry of Agriculture (MoA) stated that red chili has contributed to the Indonesian economy both locally and nationally. Chili plants from year to year experience price inflation. This price change have been influenced by several factors such as the number of requests, and changes in weather that can affect production. In this study, the prediction of chili prices was carried out using K-Nearest Neighbors (KNN) based on chili price data and weather data. Data obtained in the form of data with classes that are not balanced, so that the Adaptive Synthetic (ADASYN) algorithm is used to overcome the imbalance of data classes. From the results of classification research using KNN reached the highest accuracy of 93% but with F1-Score 0%, different from the performance of classification research using KNN and ADASYN, which was obtained 100% accuracy with F1-Score 100%.

Data mining process on weather forecasting in recent years has taken a great contribution toward human life, every aspect of it can be taken as consideration based on what kind of problem to be solved. One of its usages is to predict or forecast how much rain that will happen in some certain area just by studying the historical data of the precipitation, temperature, relative humidity and so on. Another example of weather forecasting benefit is to know what kind of crop should be planted on that time into how much profit that can be gathered from the crop harvest, it's more like economical optimization to apparently. The purpose of this research is to find out whether the Centroid Decomposition approximation approach is going to be a good alternative or not in terms of enhancing the forecasting results produced by the initial forecasting method and to prove whether the smoothing technique addition to it makes it a better procedure or not. The approximation results compared to several forecasting methods namely Linear Extrapolation, Triple Exponential Smoothing, and Moving Average. Several scenarios of forecasting interval have been made to make a fair comparison and deeper analysis of each method used in this study. The result shows that generally, Centroid Decomposition (CD) approximation process can enhance the initial forecasting method's result yet still unstable in terms of the RMSE score. But the smoothing technique addition to the CD procedure showing a great result as the RMSE successfully lowered in all given scenarios

Business analysis is performed to determine the business that are popular, with text mining can take data from several news portal in Indonesia. Text preprocessing is used to change the text title and tags on the news to be converted into weights. The weight of the data will be processed using the K-Means algorithm to be grouped into clusters and each cluster will be visualized using Word Cloud so that words that often appear as popular word identification are known. Testing uses the Silhouette Coefficient to calculate the quality of each member against the cluster. Furthermore, each member will be interpreted according to the test results. Analysis is carried out every month in 2018 with a total of 995 data with a monthly average of 6 clusters, in January were the most popular business according to the number of members from 64 data formed 6 clusters, the most member clusters were cluster 1 the Silhouette Coefficient test results are strong 0.00%, medium 65.22%, weak 30.43%, not substantial 4.35%, Word Cloud formed was a leather bag business.

Data mining applications usually use datasets with large dimensions. Unfortunately, the large dimension in a dataset affects the processing time and outcome of the classification. One solution to this problem is to select relevant features for the reduce dimensions. The selection of the right features can increase the accuracy of the classification process. This study proposes a proper feature selection model for increasing the accuracy for specific classifier models by comparing several existing feature selection models and some of the classifier. The feature selection, we use information gain, gain ratio, and correlation-based feature selection (CBFS) while the classifier we employee K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Artificial Neural Network and Decision Tree. Evaluation is done using several public databases from UCI Machine Learning Repository. Evaluation results indicate that all feature selection methods are increasing the accuracy, but Decision Tree only increases in CBFS. Based on the result, feature selection does not always improve classifier accuracy but depends on the characteristics and algorithms used.

Text mining is usually used to detect document similarities and plagiarism. The field of education is one area that is prone to plagiarism. Plagiarism can kill someone's creativity because this action does not require energy and does not have to think hard. Therefore, the act of plagiarism must be prevented from causing harm to various parties. By using matching strings on documents, it can be used to detect plagiarism. One method that can be used is Rabin-Karp Algorithm, but in several studies that have been done the researchers did not test the k-gram value and database value, in theory, this would affect the performance of the Rabin-Karp Algorithm. Therefore in this study, the selection of k-gram values and prime bases was conducted to determine the effect on the performance of the Rabin-Karp Algorithm. The results showed that the selection of gram values and prime bases affected the processing time in testing the data and the similarity values of the documents being tested. In this study the value of k = 5 on k-gram has the fastest time for the testing process, both testing with multiple data 25 and testing the data for all amounts of data the number is 300.

Social media has grown so rapidly, so people easily to share their opinions, moments, etc. There are several researches about social media, one of which is Sentiment Analysis (SA) that can also be referred as opinions meaning (OM). Sentiment Analysis focuses on the classification of patterns that is derived from words that are positive words, negative words, and neutral words. In this paper, researcher uses sentiment analysis with a machine learning approach and uses Fuzzy K-Nearest Neighbor (FK-NN) as the classification method. The dataset uses English text classification, to predicted sentiment of customer reviews about positive or negative review. The predicted results show that Sentiment Analysis FK-NN is slightly close to the results of the previous research method, namely Probabilistic Latent Semantic Analysis (PLSA), which FK-NN is 72.05% and PLSA is 76%.

A scientific journal is a scientific work that is published regularly by an organization or institution, carelessness of writing in scientific works can be considered as a form of plagiarism. So that citation writing in scientific work is important, because citations able to provide recognition of reference sources. The method used in this study is Support Vector Machine (SVM) and TF-IDF, which TF-IDF used to reduce the number of dimensions of data, so that the data can be processed optimally using SVM. The results show that using the TF-IDF method can support the SVM method in detecting citation sentences with an accuracy increase of is 0.006866436 and f-measure of 0.007994591. These results are obtained with a maximum value of k at 10.

Having a rapid growth across the world, Online Gig Economy (OGE) has the potential to reduce unemployment in Indonesia. It offers flexible working arrangement, flexible recruitment and lots of job types. Unfortunately, current existing economic and labor measurement systems are still not suitable to measure OGE distribution in Indonesia, especially for digital workers as gig worker. This research relied on web crawling and web scraping for data collection with Automatic Text Classification (ATC) for data aggregation and classification. By delving eight platforms, 2,062 active gig workers were captured from 171,033 Indonesian users in preliminary result. Their profile was distributed into some dimensions: affiliated platforms, work fields, provinces, and paid salary. By Fiverr, Upwork, and Projects.co.id as favorite platforms, most of gig workers were categorized in creative and multimedia. Considering 3.4 million as the average of gig workers' paid salary, gig economy offer competitive and promising alternative for society to get money. These findings showed the significant role of internet to achieve better live and reduce unemployment.

Apriori algorithm has been widely used to establish association rules and be applied on various areas. However, when applying this algorithm to some applications with relational database, the iteration process become ineffective. This study proposes the use of transaction temporary table to increase the time performance of apriori algorithm specifically in the scanning process of support value. The result showed that the algorithm ran faster on the process that using temporary transaction table.

The recommender system has gained attention in the past few years, because it can provide benefits for both the owner of the company and the user to get relevant items quickly. Collaborative filtering is the most commonly used recommender system method because of several advantages. However, collaborative filtering still has several issues such as cold start, sparse data, gray sheep and dynamic taste. Many studies have tried to accomplish these issues with various approaches. In general, the approach used is hybrid technique to cover the weaknesses of each other. One study has tried to accomplish these issues using a 7 block hybrid technique approach. However, the study has problem when dealing with reviews with varying length of sentences (from very short to very long) for calculating ratings, hence it can reduce the overall quality of the recommender system. This paper aims to propose another approach in calculating rating using GRU and it will be combined with other blocks of hybrid technique. In addition, new blocks will be added, namely demographic filtering. Based on the results obtained, the proposed method provides better results compared to method of previous study.

Many mobile applications that recommend attractive and customized POIs are often not adaptive enough for users who are actively traveling. Although almost all mobile applications and one concept of the mobile recommendation system have been able to adapt to users, in fact, the findings prove that this is only a small effort that mostly focuses on capturing user behavior during tourism activities. Instead, the Adaptive Mobile Recommendation System (AMRS) is proposed by carrying out all possible configurations that can improve adaptability in mobile applications. Furthermore, the AMRS was built as a new concept based on the Adaptive Tourist Recommendation System (ATRS) concept that was proposed in the past. AMRS takes advantage of the travel cycle and the concept of experiential learning as an unlimited effort of adaptivity that can be applied in a real way using the structure of the recommendation system.

The application of data mining using the datasets has a high dimension that can cause problems in accuracy and time consumption in the classification phase. One of the solutions to solve this problem is one that uses dimensional features with the right selection in the dataset. This research aims to select the appropriate feature selection for the classification process and find out whether feature selection can improve accuracy. The feature selection methods that are often used are information gain, gain ratio, chi-square, and correlation-based feature selection. This research will be done a comparison of the four feature selection methods techniques to determine whether the feature selection process can always increase the accuracy and reduce the computation time of the classification algorithm. According to the research that has been done, applying the four methods can make the accuracy of NB, K-NN, SVM, DT, and ID3 algorithm decreases. However, it reduces the computation time of K-NN, SVM, and ID3 algorithms. Using the four feature selection methods, the most influential attributes are SSLfinal_State, Having_sub_domain, URL_of_Anchor,Preffix_suffix,SFH,Domain_registration_length, Links_intags, Web_traffic, Request_url, and Google_index. According to these results, the feature selection process can decrease the accuracy of the classification algorithm. This is due to either the character of the data or the classification algorithm itself. The feature selection process can also reduce computing time so that speed up the working process of the classification algorithm used. This is because the data dimensions are getting smaller.

Immanuel Christian University (UKRIM), Yogyakarta has been established since 1982 under the auspices of the Indonesian Faith Foundation, while in its development needs are needed for the optimization of every information technology service available at the University in accordance with the times. Based on the results of the questionnaire divided in 2018, results showed that 70% of users, both lecturers, employees and students often complained about the accessibility of streaming, especially https sites, difficulties in batch vouchers, and also the rector did not need a monthly monitoring report that was useful for university accreditation accountability in improving information technology services, especially network fields. In this study optimization was carried out by overhauling the packet data traffic and VLAN-based managerial bandwidth network services used by the UKRIM campus. The reshuffle includes replacing lanes to add new ip address blocks, as well as adding external radius servers and kerio-based monitoring. The results of optimization are proven that the value after optimization is better than before optimization, this is evidenced by the throughput of both lecturers and students who change from 0.35 and 0.25 to 0.67 and 0.44. From the results of testing the hardware consumption it was found that there was an increase in the consumption of CPU Load on the Distribution Router from 5% to as much as 11%. As for the consumption rate, there is the most significant change from the download and upload side where before the optimization spent a total upload / download of 20,644 / 141 GiB and after optimization spent a total of 127,6/1333,9 GiB.

News is generally published by news providers. News providers in publishing news are sometimes not grouped into several news categories. This allows readers difficulty in finding news because the news is not grouped. This study group Krama Alus Javanese news for readers can search news according to categories with the Hierarchical and K-Means Algorithms. Research data uses news documents from the JOGJATV online news portal.In this study, the data is processed through text preprocessing. Text preprocessing consists of five processes, one of which is stemming. Stemming is used to adapt the Nazief and Adriani algorithms which are adjusted to the rules in Javanese. The method used to process the results of text preprocessing is hierarchical clustering combined with k-means. Hierarchical clustering is used to determine the number of clusters and centroids of each cluster. The results showed that the stemming process required the addition of basic words and stemming rules. While for the clustering results there are 18 clusters. The evaluation of overall cluster structure with Average Silhouette Width (ASW) shows a value of 0.9142. This value indicates that a cluster has different characteristics from other clusters so that the news documents are in the right group. The clustering results are also validated by Experts with good results, namely 11 clusters can be labeled while 7 clusters with labels are not specific.

One business that has developed along with the increase in information and communication technology is the transportation service business. The last decade has emerged as a technology-based transportation business innovation, for example Grab. It is important for a company or organization to find out about people's responses to their services or products. Public opinion on the product is not small in number, even though it is undeniable that public opinion has an impact on the company's image. Therefore, a technique for analyzing the opinion is needed so that the company can monitor and organize their services. Public opinion is classified into positive or negative sentiment classes using SVM and Maximum Entropy methods. The labeling results are then analyzed by text association to find the relationship of each information obtained. Classification with SVM method produces 89.01% accuracy. Whereas Maximum Entropy obtained higher accuracy that is 90.46%. Text associations obtained from the positive sentiment class and negative sentiment class. The results of negative reviews are analyzed for causes and consequences using fishbone diagrams for problem solving.

The number of smartphone users today has reached a staggering number. This fact leads to the growth of the mobile application market. It is essential for each developer to maintain their engagement with their user by understanding the users' needs. Sentiment analysis is an approach that can be used to classify reviews from users to help to understand the users' need. Recent method using lexicon based approach which is proposed by previous study still has poor performance due to domain limitation problem on lexicon based approach. One way to handle this domain limitation problem is by using domain specific features. This study tries to explore the impact of the use of domain specific feature in the realm of app review to the performance of the classifier. The domain specific features which are explored in this study are star rating and special words. The result shows that the use of star rating and special words can increase the performance of the classifier both on accuracy and f-measure evaluation score.

Communication in the company has a number of objectives including for internal communication, communication to business to business (B2B) market, and communication to the public market. Social media, at this point, becomes one of the alternatives as the communication media. However, today many social media are available, making it necessary for the company to map the social media in line with the communication objectives to make the information on the target. This research conducted the mapping of social media based on the needs of the company. The mapping was conducted by identifying the communication business needs based on the literature review, matching the features owned by the social media with the criteria of communication business needs, and giving a suitability assessment using Weight Decision Matrix (WDM)

Algorithm stemming of Nazief and Andriani has been developed in terms of speed and accuracy. One of the developments is Non-Formal Affix Algorithm. This algorithm increases the accuracy of stemming in non-formal affixed words. In its development, Indonesian language is used in two forms: standard and non-standard. Non-formal language is commonly used in non-formal situation such as in daily conversation, post, or comment on social media (Facebook, Twitter, Instagram, etc.). Non-formal Affix Stemming Algorithm is used to get the basic word of a non-formal word. However, this algorithm has limitation in non-formal affixed word stemming. Thus, this research aims to focus on the modification of non-formal affix algorithm to increase accuracy in non-formal affixed words stemming. The result of this research shows that modification of the combination of Levenshtein Distance and frequently used words dictionary has accuracy level of 88.33% with precision of 1 and error rate of 0.2. Meanwhile, Non-formal Affix algorithm has accuracy level of 73.3% at stemming 60 non-formal affixed words with precision of 1 and error rate of 0.26. Therefore, it can be concluded that Lenhstein Distance approach can increase the accuracy of Non-formal Affix algorithm in non-formal affixed words stemming.

Automatic text summarization can be defined as the process of presenting one or more text documents while maintaining the main information content using an automatic machine with no more than half the original text or less than the original text. Research in the field of text summarization began in the 1950s and until now there is no system that can produce summaries such as professionals or humans. This paper aims to identify and analyze methods, datasets and trends in automatic text summarization research from 2015 to 2019. The method used a systematic literature review (SLR) about automatic text summarization. The results obtained are that research on automatic text summarization is still relevant to date. The extractive approach is still in demand in the past three years because the extractive is easier than abstractive and the opportunity to combine methods is still open, for example using a neuro computing approach, namely the emergence of a new DQN method (Deep Q-Network) which shows comparable results and even better. The text summarization research trend has also undergone a slight change in the past three years where new things have emerged that are trends that are leading to optimization, how to optimize text summarization performance in order to get high accuracy

The study of facial expression has been exist since the Aristotelian erain which it explains the expression into a complicated nonverbal research. Expression signals are instinctive behaviors, not learning outcomes, so machines will not be easy to recognize the varied expressions of people. In this study, the detection of accuracy levels in the form of predictions of class expressions of pleasure, sadness, surprise, disgust, neutral, anger and fear in real time with captured face images from the camera of an Android mobile phone isconducted. This research is important as the first step in detecting individual lies in terms of non-verbal communication. Broadly speaking, the research process is divided into 2, an approach to detect face position with Viola Jones and extraction and classification with deep learning Convolutional Neural Network to produce predictions of expressions from machine learning. In CNN there are several layers for the filtering process by conducting the learning process to find the best representation. Datasets used face image capture in real time using a mobile camera with a total of 41 try expression detection. The result of the study shows that the performance of the Viola Jones and CNN method produces the highest level of accuracy, that is 98,7% for happiness expression with 9 times detection.

Keywords: facial expression, face detection, viola jones, convolutional neural network, android.

Tests are one form of evaluation of the learning process to measure success in the teaching and learning process. Written tests can train in conveying information verbally. Manually correcting the answers to essay questions requires a lot of time, but there are many difficulties when correcting essay answers manually. To correct answers quickly and accurately, a system that is able to correct essay answers is needed. Rabin Karp is a very efficient multiple pattern search algorithm looking for sets with many patterns. The results of this system are expected to be able to correct the answers to essay questions more easily and accurately. Rabin Karp algorithm will look for a pattern in the form of a substring in a text using hashing. Stages of the workings of this rabin karp algorithm include preprocessing, then divide the text into gram grams determined by k-gram, calculate the hash value with the rolling hash function of each gram, then determined the same gram value from the member's answer and answer key and the latter determines the level of accuracy of the answers from the member and the answer key using the Dice's Similarity Coefficient. Results The similarity of answers has a range that can be used for essay assessment systems.

Instagram is one of the most popular social media in Indonesia. With instagram, users can share their moments of life in the form of photos or videos. Instagram users can follow each other. But when a user already has a lot of followers, many instagram accounts also respond to posts with comments that can be categorized as spam. Spam comments are usually found in every account post that has a lot of followers, especially public figures in Indonesia and of course this is very annoying. Instagram has provided services to delete or hide comments, but a model is still needed to detect comments that are spam or notspam.

The Naive Bayes algorithm will look for the probability of each class when the comments are inputed. Before the comments probability are calculated for each class, comments will be processed through the preprocessing stage, namely casefolding, cleaning, tokenizing, and stemming. After knowing the probability value of each class, then the probability value will be compared. If the highest probability value is a comment that is hypothesized as spam class, then the comment will be labeled as spam. If the highest probability value is a comment that is hypothesized as notspam class, then the comment will be labeled as notspam.

Model evaluation measures showed good results: precision of 0.72, recall of 0.98 and F1-measure of 0.83, therefore spam comments classification using Naive Bayes can be considered as successful.

Hate Speech is a problem that often occurs when someone communicates with each other using social media on the Internet. Research on hate speech is generally done by exploring datasets in the form of text comments on social media such as Twitter, Facebook and MySpace. This study aims to improve the performance of the Random Forest method in detecting hatespeech and crude speech. In this paper the researcher uses a twitter hate speech and offensive identification dataset that is classified using the Random Foresh method which will be compared with the results of its accuracy with AdaBoost and Neural Network to detect hatespeech and crude speech. The detection results of hatespeech and crude speech identification resulted in an accuracy of 0.722 for the Random Forest method and 0.708 using AdaBoost and 0.596 using the Neural Network method.

Current information is very easy to obtain by utilizing internet facilities wherever and whenever. On the other hand the information obtained from the search engine is all things related to the keywords that are searched for. This causes users to have to filter to get relevant documents. Therefore we need a way to classify the amount of information available, which is needed by the user, making it easier for users to get the desired documents. Information retrieval system is a system that is used to find information that is relevant to the needs of its users, by applying the system problem information retrieval Physics force can provide relevant results according to the needs of users. There are two main processes in information retrieval systems, namely indexing and retrieval. One of the weighting algorithms is the TF-IDF algorithm which is influenced by the frequency of occurrence of words in each online document and the frequency of online documents that have the word. The purpose of this study is to develop and implement automatic indexing to build document search systems in a storage system document with the concept of information retrieval.

The Philippines as an archipelagic nation has numerous number of dialects per island that hampers local tourist communication. Cebu being one of the top tourist destinations by locals also has a vast language hurdle compared to other local dialects. Because of a huge language differences of native Cebuano and Filipino speakers, travelers in Cebu often experience difficulty in expressing their needs and requests to the island locals. The solution is a mobile-based translation system with object detection for travel assistance. The system uses Neural Machine Translation to translate Filipino to Cebuano language and vice versa based on the user's keyboard input, extracted text strings from detected image, and the detected object itself. The Filipino-Cebuano model obtained 31.1 BLEU score, while the Cebuano-Filipino model obtained 31.6 BLEU score.

There are many languages in the world, each language has its own characteristic. This makes language as an interesting topic as research object, especially in text mining topic. Language, in the form of text, needed to be preprocessed first to get its normalized form. Abbreviated words is one of the problems in text mining, that makes the text cannot be taken into further process because the system didn't know exactly what the meaning of those abbreviated words. This research purpose is to developing Indonesian abbreviated words dataset, then it can be used to normalizing any abbreviated words in Indonesian language. Crowdsourcing was selected as a method to develop the dataset, because language consist of qualitative data, then it can only be done by participation of humans. Experiment result show that the dataset can normalizing any abbreviated words almost perfectly with 90.85% successful percentage.

Social media nowadays is a platform to many things and has become a place to delivers opinions. Opinions in a form of hate speech are one of the problems that authorities find hard to solve, because of its number and variations. Because of that, a system will be made to detect hate speech on Instagram comment section using Maximum Entropy methods. Instagram is the object of this research because it is one of the most popular social media in Indonesia. Accuracy generated from this research is 86.67%. This system is expected to determine whether a comment on Instagram is a hate speech or not.

Based on data from the Indonesian Central Bureau of Statistics which was updated in February 2018, the percentage of the workforce towards the working age population reaches 69.20%. The Open Unemployment Rate in Indonesia reaches 5.13% or 6.87 million people. Prospective workers, especially new graduates who have a small level of work experience will have difficulty in marketing their expertise because they are unable to compete with experienced workers. This research is used to facilitate the selection of prospective workers/job seekers. It developed and equipped with a Decision Support System Employee Recommendation using Fuzzy Sugeno Method. Fuzzy Sugeno method is used to process job data and job search data based on variables of age, education level, certification of expertise, and work experience. Job data processing determines the maximum and minimum value limit for job seekers to get recommendations for applying jobs. The same variables of job seeker data and job data produce a value of received percentage in job openings. Job seekers will get job recommendations accompanied by a value of recieved percentage. Job seekers who have applied for a job opening will be displayed sequentially based on the value of received percentage at the job opening therefore they can find the recommendations of the workers.

Tourism is one of the activities carried out for recreation or leisure in a place with a variety of purposes and objectives. In Indonesia, many cities provide attractive tourism places, and one of them is the city of Yogyakarta. Because it has interesting and diverse tourism places, Yogyakarta is in great demand by local and foreign tourists. Thus to be able to maximize the visits of tourists who come to Yogyakarta, we need a system that is able to provide information on tourist attractions to tourists precisely in accordance with what the tourists want. The proposed system uses the Fuzzy Logic method and Floyd Warshall Algorithm which are combined, so as to obtain results in the form of recommendations for tourist attractions based on the costs of tourists, the length of time and distance needed to reach the tourist attractions.

Fault diagnostics have become a primary concern within the domain of power system engineering, particularly for distribution utilities. Early and accurate fault diagnostics within a power system is crucial for outage mitigation. The classical approaches for fault diagnostics are limited to the checking of some measurable output variable, the actual fault location, and the historical data related to the involved real-time applications. A new approach for fault diagnosis within the power system network, based upon Convolutional Adversarial Neural Networks (CANNs), is presented within this paper. The model combines the properties of Generative Adversarial Network (GAN) with Convolutional Neural Network (CNN) for a hybridized architectural. The CANN approach better contends with the complexity of a network as well as more robustly identify incorrect or missing faults as well as multiple-fault events for enhanced fault diagnostics. The results indicate that the new approach is more successful in the diagnosis of simultaneous faults as well as better anticipates the problems at the nexus of transmission and distribution system amidst a blackout.

Numerous studies indicated the importance of drowsiness detection on the road since it can lessen accidents most especially to the commuters who take long hours of travel before reaching their destinations. Somnolence or drowsiness is a state of strong desire for sleep as a result of many factors which include stress and fatigue due to certain types of medication, sleep disorder, and boredom through repetitive tasks such as driving for long periods. Currently, deep learning is widely used in machines or devices. Thus, deep learning is improved to series studies and trends nowadays. The created new system includes the detection of patterns in the facial features (eye closure, nodding/head tilting, and yawning) of a driver using a camera and forwards it to the SqueezeNet deep neural network; a sound alarm alerts the driver when the pattern exhibited by the driver's facial features is analyzed as drowsy. The system has been tested and resulted in an overall accuracy of 97%

This work studied the comparison of LSTM, ConvLSTM and CNN-LSTM model, that was applied for time series forecasting. We created the LSTM, CNN-LSTM, ConvLSTM model and configured the optimal parameters by using hyperparameters optimization techniques. All models were applied to two different datasets for forecasting the number of patients in the future. This work also applied the SeLu and ReLu activation function to avoid the problem of gradient vanishing and improve the self-normalizing. The results indicated that two models had skillful, and made the reliable forecasting in two datasets. This work benchmarked the model performance by calculating MAE, RMSE, and sMAPE, which was acceptable in all case study. The CNN-LSTM model with SeLu activation function gave highest forecasting efficiency for the data contain seasonal variation. LSTM model with SeLu activation function gave highest forecasting efficiency in the case of non-stationary data. In contrast, LSTM model operated faster, took a lower performing time, and lower CPU consumption than CNN-LSTM and ConvLSTM model.

The medical services are an important requirement for a human to be healthy. The most hospital has to ensure the availability of resources for the medicine needed at all times. In general, most hospital make an estimation for a future demand by forecast a number of patients to provide the medical services in the future. Therefore, the accurately forecast a number of patients would be valuable knowledge for managing. This work proposed the CNN-LSTM model, which was a combination between CNN and LSTM to forecast the number of patients who used a hospital services. The CNN model was used to interpret, and extract the features from subsequences of input data, Then, it provided this sequence to LSTM model for interpreting and making a forecast. The CNN-LSTM models were created, and learned the data, and applied to forecast on two different datasets. The results indicated that CNN-LSTM model made the reliable forecasting. The result showed RMSE, and MAE of two department were very low in two experiments. Forecasting the number of patients can help hospital to estimate the service demand, make a better policy for managing the medical resources on demand, and improve the efficiency of medical services for the future.

This study implements a semi-join approach with the SDD-1 algorithm to obtain a fast query process and find out the benefits and costs of the semi-join method. The subject of this study is a distributed banking database with 8,910 data sets. Optimization is done with three (3) query processes that obtain a 600% speed increase, for the first query process it resulted in an increase of 300,4, the second query process resulted in an increase of 144,9, and in the third query process resulted in an increase of 154,9. After obtaining the optimization of the query through the semi-join method, the benefits and costs obtained from the three processes of query optimization, that is, the first query process benefits 2,711,168 at a cost of 806, the second The query process obtained 3,304 benefits at 238 and in the third query process it benefited 3,069,888 at a cost of 615. The process data shows that the application of a semi-join method with the SDD-1 algorithm for the optimization of distributed database queries effectively increases the query speed and obtains great benefits and low costs.

In this paper, we compare the performance of Single Board Computer (SBC) cluster between using local storage and using Network Attached Storage (NAS) as Hadoop Distributed File System (HDFS) data storage. We build a Hadoop cluster with one master node and four slave nodes. The node used is SBC with Micro SD Card as local storage. For benchmarking, we use terasort and testDFSIO programs that available on Hadoop. The results indicate that the performance of SBC for the Hadoop framework increased by using NAS. The processing time needed to run a terasort program is faster with NAS than local storage. The greater the data processed, the greater the percentage of processing time. And the comparison of read speed using both is almost the same but write speed when using NAS is almost twice faster than using local storage. We also use the Performance Analysis Tool (PAT) to measure the performance of SBC. PAT collects CPU performance metrics on SBC when running a terasort program. The results show the CPU utilization of SBC is better to use NAS than local storage.

In the defense industries, robot technology and the use of robots for defense system are growing rapidly. A robot with Remote Control Weapon System (RCWS) needs a stable Direct Current (DC) voltage for accelerating its movement, reading the data precisely, and controlling the DC-motors accurately. In this paper we did a feasibilty study of a robust buck converter operated in Continuous Conduction Mode (CCM) with Sliding Mode Controller (SMC) for hybrid diesel-electric drive. SMC is a non-linear control that changes the system dynamic of a non-linear system by employing discontinue control signal (equivalent control) and continue control signal (switching part). It forces the system to lie on the sliding surface for the system in the normal operation. SMC is used to make the buck converter generating stable output voltage to support such robot with a hybrid diesel-electric drive. SMC acts as Pulse Width Modulator (PWM) which triggers the buck converter. From the simulation results, we concluded that SMC successfully plays its role to make the buck converter maintaining the stability of the required voltage level for the RCWS robot.

With an increasing number of student body at Telkom University, office automation is one from many possible solutions to provide better and personalized service to students. This automation also includes library services, covering self loan, self return, and self pickup, which commonly use RFID (Radio Frequency Identification). The problem arises in RFID-based self service system for library automation, which is how to ensure that ID card of the patron truly belongs to the person using the service. This problem follows the self-loan service, since the ID card of the patron is used only when borrowing the collection. Another problem is how to ensure that the borrowed collection is the actual collection registered in the LMS (Library Management System). This research proposes a business process of a self loan system design that minimize fraudulent practice in borrowing collections by merging image capture of the patron and the borrowed collection where librarian is not necessarily present for circulation.

A flight retiming problem is a problem to retime departure times of flight schedules so that the robustness of the schedules can be improved. By minimizing total propagated delay along aircraft routings, the problem can be formulated as a linear integer programming model. in this paper, we discuss about how to model the uncertain flight retiming problem using robust optimization. We derive the robust counterpart of the problem by choosing uncertainty box and ellipsoidal uncertainty sets. The modeling results show that we obtain a linear integer programming model for box uncertainty set and a conic quadratic programming model for ellipsoidal uncertainty set which are classified as computationally tractable problems.

Dengue fever is a disease caused by infection of the dengue virus. The virus is spread by a species of mosquito, namely Aedes Aegypti. Dengue fever can cause death to the sufferers. One of the possible methods to minimize the spread of dengue fever is controlling the cycle of Aedes Aegypti itself. This article simulates the use temephos (some chemical substance) and fumigation to control the population growth of the adult mosquitoes. The first control, temephos, is given on the habitat of mosquito eggs and larvae. The second control, fumigation, is done by fogging on the habitat of mosquitoes. The problem is to determine which methods, i.e. the use of temephos or fumigation, are more effective to suppress the growth of the mosquito population. Here, we use a dynamic programming method to model the problem and to get an optimal control that can minimize some determined objective function. The numerical solution shows that both controls can be used to reduce the spread of the disease. However, it is apparent that fumigation is more effective.

Droplet as the basic element of spray phenomenon, has drawn engineering researchers to investigate the characteristics. Spray cooling, spray painting and spray fuel combustion are only few that mentions the applications in engineering. Modeling and simulation of droplet is very important subject in line as experimental field. Lattice Boltzmann Method (LBM) that emerged as non-conventional tool in computational fluid dynamics (CFD) is used in this work. The multi-phase fluids that involved were treated by pseudo-potential method. The results in this work was validated through spurious current measurement. The floating droplet simulation plotted in high density fluids inside droplet and low density fluid outside. The interface between the two-different density than plotted as average of both high and low density.

During the measurement of the Ozon vertical, the connection between the balloon and the ground segment sometimes lost. It is because the antenna is not pointing to the balloon. To point to the balloon, the antenna orientation is needed to be changed manually by an operator. The operator needs to know the last position of the balloon fastly and accurately. The other aspect, to know the location of the balloon is very important for the Air Navigation Agency. Air Navigation agency needs to know the current position of the balloon to make sure the airplane will not crash with the balloon. Without knowing the location of the balloon, the above issues, cannot be solved. In this project wants to give a solution, how to give the correct location of the balloon to the operator and Air Navigation department. How to get the information easily and immediately. The project showed able to give information about the balloon location in a real-time manner. The application that has been made able to show the balloon current location in a user-friendly, via a map in the browser. To add other useful information regarding the balloon location, in this project also do a prediction on where location the balloon will be flying. The real data result and the prediction has been analyzed in this project.

This paper considers an MPC-based ship controller for trajectory tracking and collision avoidance. The trajectory is obtained by generating a Dubins path and avoiding a static obstacle. The ship's model is given by combined 2-DOF ship dynamic and kinematic model in the form of error w.r.t. the reference. There are two scenarios of trajectory which must be followed by the ship. Computational results show that MPC can control the ship to follow the trajectory in both scenarios.

Nanosatellite is one of satellite technology that can now be developed by various types of circles, one of which is in university scaled. Currently Telkom University is developing nanosatellite for research purpose. Nanosatellite system can be run with the support of several subsystems. One of the subsystems is On Board Data Handling (OBDH) as the main control. The presence OBDH subsystem is for processing system data, such as get the housekeeping data parameters to monitoring and determine the nanosatellite health system. The parameters consist of temperature, gyroscope orientation, and magnetic sensing. In Tel-USat, the On Board Data Handling by using 32-bit ARM Cortex M3 as main controller with several sensors to collect the parameters data. To support the running of reliable system process, Real Time Operating System (RTOS) can be applied to manage a lot of tasks with the concept of multitasking and fast processing time.

In this article, a robust model predictive control is applied to calculate the optimal decision which is the amount of the product that should be ordered in an inventory system in order to satisfy the demand where the delivery process of the ordered product is imperfect. The term imperfect means that not all of the ordered product amount will be received. It means there are some product(s) which is damaged on the delivery process or defected by the buyer who is the inventory system's decision maker. The amount of the damaged product is uncertain and we assumed it as a noise in the state space model. We have formulated the mathematical model of the inventory level as a linear state space with the amount of the ordered product as input, the demand value and the damaged/defected product as measured disturbances in he state equation, and the inventory level as the output. To obtain the optimal input, we have used the robust predictive controller to obtain the optimal input i.e. amount of the ordered product for each time period. Computational simulations were performed in order to simulate the system and observed the dynamics of the input and the output. From the results, the optimal product amount order was obtained and the output response which is the inventory level was as desired.

In this paper, position control of a ship-mounted two-DoF manipulator subject to the motions of ship itself imparted by unidirectional long-crested random sea waves in heading sea is proposed. PSO algorithm based PD controller (PSO-PD controller) is proposed as mathematical model based control method under recursive Newton-Euler (RNE) equation. Ship motions namely surge, heave, sway, roll, pitch and yaw motions are considered as the disturbances in exciting the mounted manipulator. Results reveal that the excessive vibration in the torque of every joint of the manipulator due to the change of sea state can be suppressed effectively until sea state for code 6.

This research is implemented and controlled a wonderful vehicle. This vehicle has only two wheels to balance itself and driver. By leaning body of the driver, the Segway can move to forward and backward. Segway is the platform which applied to create concept to improve control algorithm because the Segway model is nonlinear model and challenge to control. The platform is rebuild from the commercial Segway by maintain only structure and original electronics are removed. The Segway dynamic model is solving by newton Euler method. The model is linearized by approximating about operation point. The challenge in control Segway vehicle which research focus is the driver weight can be always change. By this condition, Segway can be unstable. Then the modified LQR is implemented to control the Segway. The simulation and experimental results are conducted in this research.

There are various path planning methods for mobile robots, one of them is a-star algorithm. In this paper, we proposed a more smooth a-star algorithm. The smoothed path is easier for nonholonomic mobile robots to be tracked. Simulation and experiment verify that the smoothed a-star algorithm can be implemented properly for nonholonomic mobile robots.

Last few years, some researchers have conducted research on Fractional Order PID (FOPID) controllers that produce better system responses than conventional PID. In this paper, look for new parameter values of fractional-order PID in the electro-hydraulic servo valve system and observe system response performance. The electro-hydraulic servo valve system (EHV) is widely used in industries such as aircraft. Judging from the importance of EHV in the industry, it can have a significant impact if it has efficient performance. To improve the performance controller from conventional PID is converted into FOPID controller to find system performance with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The results of the simulation provide rise time, settling time, and overshoot values to analyze, expected to have a faster and better response than the previous controller.

Battery is one of the main parts in Electrical vehicles as it serves as energy source. In order to prolong battery lifetime and optimally operate the battery, battery performance needs to be monitored and controlled using Battery Management System (BMS). One among important BMS functions is to determine the amount of remaining energy capacity available in a battery, represented by State of Charge (SOC), through estimation. The accuracy of the SOC estimation is important since it is used as indicator to prevent battery from failures due to overcharging and over discharging. One of the SOC estimation methods is Coulomb counting (CC) which is easy to implement. However, a false initial value of the SOC in this method will degrade the accuracy of the SOC estimation. In this paper an SOC estimation method is proposed by using CC method combined with recursive least square (RLS) algorithm and Proportional Integral (PI) controller to correct the error of SOC estimation due to false initial value of SOC. The result indicates that the proposed method is able to correct the SOC estimation error due to false initial value of SOC with error of SOC estimation equals 0.498% using Mean Absolute Error (MAE).

Quadrotors offer many interesting features and have great potential to be used in many social applications. Unfortunately, due to its nature as an underactuated system, it can be made stable by itself. Hence, such a control strategy is required. This paper presents the optimized PID controller with Firefly Algorithm (FA) for quadrotor in virtual environment. The results verify that the optimized PID controller succeeds to regulate the altitude of quadrotor in the real physics-like of virtual environment.

The purpose of this research study is to construct and design a six degree of freedom (DOF) that may contain a number of robotic manipulators between the position and speed control. At the same time, the study of kinematics in robotics involves, among other things, the manner in which the coordinate frames attached to the various parts of the mechanism change as the robot articulates. Manipulating several a servo motor assembly parts, as an experimental and simulation case in this paper, is studied to evaluate and performance results as well as improving the robot stiffness is discussed.

Completing a CCTV project on time and within budget is not an easy thing. Scheduling and planning in the CCTV project hold an essential role in predicting the aspects of time and project costs. The success or failure of a project can be viewed on the initial process of CCTV planning, including projecting the crash because of the absence of optimization in terms of plan which led to the in the effectively of the project. This research aims to find the trade-off between cost and optimal time required to complete the project of CCTV by using Critical Method (CPM) and Project Evaluation and Review Technique (PERT). CPM and PERT are used to make working time and cost of the project more effective and efficient. The evaluation of this research uses data about the project time and expenses from CV Adisatya IT Consultant, a software house based in Kediri, East Java. The expected duration is 73 days, and the normal cost is Rp 207.858.300. However, by using CPM and PERT, the optimal time is 47 days with the price Rp. 168.608.300. The conclusion is CPM and PERT make the working time and cost of a project effective and efficient by reducing the completion time and cost up to 23 days and Rp. 39.250.000.

The advancement of wireless technology has revolutionized the legacy microwave spectrum into dense and nearly-fully occupied frequencies. Usage of the higher frequency spectrum is necessary to fulfill the increasingly more speed and low latency communication system. Terahertz (THz) frequency is one of the encouraging alternatives, yielding higher bandwidth capacity, data rates, and intrusion-free from other occupied frequency spectra for the next generation applications such as 5G communication. A considered challenging issue in THz regime development is to provide efficient generation and detection of THz waves. Its particular characteristic between electronic and optical domain and tiny wavelength creates its own complexity in designing THz source and detector. A bolometer as one of the THz wave detector can be chosen to detect THz waves and convert it into heat form. In this research, we studied a bowtie antenna-coupled bolometer as a THz wave receiver. A transmission line method is introduced to improve the impedance matching between bolometer and bowtie antenna. The analysis is taken by considering the surface current and dissipated energy on the bolometer with various transmission line lengths to obtain optimum energy absorption. One THz plane waves propagate on the proposed design simulated using CST Microwave Studio simulation system. The simulation results show that the transmission line length of 9 μm can increase the surface current and dissipated power by a factor of 81% and 13%, respectively, compared to the initial design. The results show that a proper configuration for the antenna length and transmission line is necessary for a maximum energy transfer from the antenna to bolometer.

Imaging Technologies has been widely used for medical applications. Among various technologies, THz imaging offers a safe imaging technique for human tissue. However, in such high frequencies, antenna design for radiating and receiving take more attention due to dimension constraint and very small wavelength. In this paper, we study rectangular linear array microstrip antenna to obtain proper beamwidth and high gain used for a THz imaging system. The considered resonant frequency is 312 GHz. The design has been simulated by using CST Microwave Studio. The results show that the three elements provide gain, horizontal beamwidth, and vertical beamwidth of 11.25 dB, 77.5 degrees, and 26.6 degrees, respectively. Those results can be considered to obtain low power and good resolution in a THz imaging system.

This paper describes the effects of modulation techniques and the number of pulses on low power dissipation in a single-phase inverter and a multilevel inverter. Multilevel inverters and single-phase inverters given pulse input use PWM (Pulse Width Modulation), SPWM (Sinusoidal Pulse-Width Modulation) and PWL (Piecewise -- Linear) modulation techniques, either in the form of square waves or multilevel pulses paired with the appropriate LCL filters. produce an inverter circuit that has a low THD (Total Harmonic Distortion) and high efficiency. The use of transformers is also mentioned in this paper to increase the source voltage at an output of 48 volts to around 220V-227Volt. Simulation results show that the effect of simple modulation switching techniques, such as PWM generated square waves, is simpler and more efficient if the right filter is used. The results of the simulation in PSpice show that of the switching techniques control in inverter system with fit filter can reduce the THD up to 1.81% and the efficiency is very high reaching of about 98%

This paper presents a comparative study of DC-DC converters with boost methods from several different circuit topologies. In several studies it has been proposed the circuit topology with sufficient results in a voltage doubling system, so to determine the circuit with a proven level of reliability it is necessary to do a more in-depth study of the overall topology series using SPICE programming. The overall topology is compared with analyzing the Maximum Boosting voltage produced and the efficiency obtained. Flow measurement and also power are added to the test. The best test results were selected and proposed as DC-DC converters which can be applied in environmentally-based power generation systems. This research is a promising solution in the future as an effort to overcome the electricity energy crisis.

A 40% survey of induction motor damage is caused by disturbances related to bearings or bearings. The use of a long period of time associated with certain operating patterns and aging early is not impossible that the induction motor will be damaged. Therefore, it is necessary to have an initial diagnosis that detects a disturbance in the induction motor rotor, so that repairs can be made faster and responsive before a larger disturbance occurs. In this study, it discusses the technique of detecting damage to an Outer race bearing in an induction motor by using an initial flow analysis in transient conditions. The system used is based on decomposition wavelet transform as a signal processing device so that it can detect the motor in a healthy state or is damaged. System testing is carried out in several conditions, namely conditions without load and burden. In addition, the conditions given are defects ranging from 1BB to 3BB with a diameter of 2mm. The test results prove that decomposition of wavelet transform is able to detect differences in conditions in normal or damaged induction motors in the rotor rod.

State of Charge (SOC) is a component of Battery Management System (BMS) which gives information on the remaining capacity of a battery. For monitoring purpose, SOC estimation is represented as ratio of the remaining capacity to the nominal capacity of a battery. In this paper, SOC estimation is carried out by using Open-Circuit-Voltage (OCV) method, i.e. converting OCV estimate to SOC estimate based on SOC-OCV relationship. For comparison purpose, Thevenin battery model and simple Battery model are considered. The OCV estimation is conducted by using Recursive-Least-Square (RLS) algorithm with forgetting factor in which the model parameters are also simultaneously estimated. To demonstrate the effectiveness of the proposed method, experiment is utilized by employing hardware devices for measuring terminal voltage and current of battery which serve as inputs to the RLS algorithm. Accordingly, a forgetting factor of 0.985 for Thevenin battery model and 0,97 for Simple Battery is obtained to yield accurate OCV estimation with OCV estimation error of 0.014% for Thevenin battery model and Simple Battery Model 0.012%.

Thermoelectric is an alternative cooling device over vapor compression refrigeration system, because it is more environmentally friendly and compact. Thermoelectric has two sides that functions as a hot reservoir and cold reservoir, and utilizes electrical energy to pump heat. In this research, testing of the thermoelectric coolant heat of TEC1-12706 at low temperature, low temperature is meant below the ambient temperature, to obtain the value of cooling capacity and the performance coefficient at low temperature. There are three parameters measured, there are current, voltage, and temperature. The hot side temperature of TEC1-12706 is kept below the ambient temperature by placing the hot side on the cold plate of the evaporator vapor compression refrigeration system. The result, it is obtained that the performance coefficient value and cooling capacity are increasing as the input power value increases in TEC1-12706. The value of Cooling Capacity is between 0.24 to 2.96 watt; while the value of COP is between 0.56 to 1.05. This shows that TEC1-12706 is good enough to be use in low temperature, below the ambient temperature.

Battery Management System (BMS) is necessary in order the batteries to work properly. One important item in BMS is State of Charge (SOC), which indicates charge level of the batteries and belongs to internal states of battery. Practically speaking, the internal states of battery can not be measured directly. Thus, SOC has to be estimated. Moreover, it is possible that its internal resistance changes while the battery is being used. In this paper, nominal value of the internal resistance is acquired from parameter identification by utilizing experimental setup for lithium polymer battery. Subsequently, through simulation, the internal resistance is set to vary around its nominal value and Kalman Filter is utilized to estimate the SOC. The estimated SOC from Kalman Filter is then compared to one from Observer and Coulomb Counting under the same condition for verification purpose. Simulation verifies that Kalman Filter performs better than Observer and Coulomb Counting for SOC estimation under varying internal resistance.

Regenerative Braking System (RBS) converts kinetic energy into electrical energy by using a motor and functions as a generator when decelerations occur. Regenerative braking is an effective alternative to increase the driving range of a vehicle and can save around 8% - 25% of the total energy used by a vehicle. The purpose of this research is to produce a design of the FRCS Braking System (Flywheel Energy Regenerative Capture System) and its system topology. The design of this system is to optimize the download of vehicle kinetic energy based on the duration of energy transfer that occurs. This study focuses on the flywheel energy download system and is done by numerical simulation using MATLAB software. The method to be designed is to apply 2 types of flywheel generator topologies

Inpatient service is one of the health services at the Islamic Hospital (RSI) XYZ which requires a lot of nursing medical personnel in the work process. In practice, not all nurses at RSI XYZ know that with all forms of medical action that must be done if needed, often senior nurses or nurses who know better must intervene to overcome the problem of medical treatment of patients who are not necessarily part of their work. So if one day nurses who have good knowledge want to get out of the job (knowledge walkout will occur), then it will cause chaos in the inpatient service process. Based on these problems, it is necessary to design a knowledge management system using Big data as the main basis in the knowledge creation process until it can be accessed. This design is carried out using the Knowledge Management System Agile Implementation Methodology (KMSAIM) which prioritizes the initiation process in each problem domain so that each component has the right solution, so that the resulting design will be more efficient and in accordance with the existing problem domain. The results of KMS's design are in the form of a website-based Knowledge Management application that has file sharing features, discussion forums to share medical experience and discussion regarding SOP updates, and document search features needed in the inpatient unit of RSI XYZ.

The availability of e-participation service is an important part of e-government. This service is expected to be able to accommodate community participation in local development. The Rembugan Jateng is an e-participation system currently available in Central Java Province. However, its existence has not been widely used by the community in giving proposals to the government. The redesign of the Rembugan Jateng system has been carried out using the User-Centered Design (UCD) approach that is expected to increase the value of the user experience. After redesigning, the system is evaluated using the User Experience Questionnaire (UEQ). The results obtained from 16 respondents show an increase in the user experience value. The attractiveness, perspicuity (convenience for users), efficiency (efficiency or speed and neatness of a product), dependability (reliability and accuracy), stimulation (the level of user interest in the product), and novelty (an aspect of innovation or novelty) increase by 0.12, 0.40, 0.2, 0.03, 0.08 and 0.42, respectively. It is expected that an increase in user experience value will increase the number of community proposals for development in Central Java.

Hospitals, as one of the health care institutions have challenges in providing better health services. In increasing the value of its business, many hospitals open branches in various regions so that it requires integration from each branch. However, there is often a failure of IT implementation because there is no alignment with the objectives of the organization where each hospital incorporated in the group still works independently. Enterprise Architecture is one solution to overcome the failure of IT implementation in organizations. This study aims to build Enterprise Architecture in the hospital group. The case study to support this research was conducted in one of the hospital groups in Indonesia namely Kasih Group. The method used is a combination of ESIA and TOGAF ADM. The results of this study are the Enterprise Architecture design in the hospital group which consists of 5 phases, namely: business engineer re-engineer, initialize architecture, business architecture development, information system architecture and develop technology architecture.

The application of data mining using the datasets has a high dimension that can cause problems in accuracy and time consumption in the classification phase. One of the solutions to solve this problem is one that uses dimensional features with the right selection in the dataset. This research aims to select the appropriate feature selection for the classification process and find out whether feature selection can improve accuracy. The feature selection methods that are often used are information gain, gain ratio, chi-square, and correlation-based feature selection. This research will be done a comparison of the four feature selection methods techniques to determine whether the feature selection process can always increase the accuracy and reduce the computation time of the classification algorithm. According to the research that has been done, applying the four methods can make the accuracy of NB, K-NN, SVM, DT, and ID3 algorithm decreases. However, it reduces the computation time of K-NN, SVM, and ID3 algorithms. Using the four feature selection methods, the most influential attributes are SSLfinal_State, Having_sub_domain, URL_of_Anchor,Preffix_suffix,SFH,Domain_registration_length, Links_intags, Web_traffic, Request_url, and Google_index. According to these results, the feature selection process can decrease the accuracy of the classification algorithm. This is due to either the character of the data or the classification algorithm itself. The feature selection process can also reduce computing time so that speed up the working process of the classification algorithm used. This is because the data dimensions are getting smaller.

This paper reports the effect of composite oil on breakdown voltage. The composite oil consists of palm oil and diala-b oil with various ratios. The objective of the experiments is obtain a breakdown voltage that was in accordance with the minimum standards of liquid insulators. It shows that the water content and acidity affect the breakdown voltage. The breakdown voltage of all composite oil samples that have treatment process meets the IEC standard No.56-1991 and SPLN 49-1:1982.

Distributed generation (DG) is one of the new approach in the installation of power plants which the plant location is no longer centralized, but it was spread on the distribution of electricity feeders. Nowadays, the application of distributed generation has increased because it is related to smart feeders and it was related to the development with clean, renewable energy technologies based on solar and wind. This supports government programs to use renewable energy in 2025 by 23% where 35,000 MW of 23% is solar panels in Indonesia. Large-scale development of distributed power plants can affect the conditions of the distribution feeder. Therefore, it is necessary to know the impact of the installation of distributed power plants. This study was conducted to analyze the effects of the penetration of the distributed power plant in this case photovoltaic in 2 cases of installation location. First, the distributed generation was placed in the center of the feeder. Second, the distributed generation was placed at the end of the feeder. Simulation in this study was conducted using the Open Distribution System (OpenDSS) software with real data of rural distribution feeder in Bantul Regency, Special Region of Yogyakarta, Indonesia. The 20kV distribution feeder was used in this feeder, and it was divided into one feeder. The results of this study are expected to provide information about the impact of the power plant penetration spread on voltage and power losses in the distribution feeder, especially in the Bantul feeder. The results of the study show that the installation of distributed power plants enhances the voltage profile. Keywords-distribution system, distributed generation, open distribution system, photovoltaic.

This paper is focused on the development of a signal generator Sinusodal Pulse Width Modulation (SPWM) which is used to control a full wave power switch on a single phase inverter based on a Field Gate Array Programmable (FPGA). FPGA can operate in parallel with quite high computing capabilities. Quartus Prime 18.0 Lite Edition and ModelSim are software used to describe and simulate SPWM generating units. The digital signal generator of sine and triangle signals with the Look Up Table (LUT) method which is stored in FPGA internal memory. Mosfet IRFP460 is used as a semiconductor switch on a single phase inverter with a full brige configuration. The system is first simulated using PSpice A / D Lite before being tested on a single phase inverter device.

Power quality is interesting topic to discuss, because in the industry, buildings and electricity power systems there are always problems with power quality. For example Harmonic always arises due to nonlinear loads. Examples of nonlinear loads are materials from semiconductors and electronic devices. In industry, household, and electric power systems always use electronic devices that become contributors to harmonics. The object of the research is Departement of Electrical Universitas Negeri Malang. In the department there are 5 orders that value Voltage Individual Harmonic Distortion (VIHD) of more than 5%, that are 5, 7, 11, 13, and 17 order. Generally one filter just can mitigate one dominant or highest harmonic orde. But it is difficult to mitigate harmonics if there are high 5 orders simply by using a passive filter. This article will discuss harmonic mitigation by combining several passive filters and capacitor banks on one bus. The goal is to optimize harmonic filtering so that it can reduce the value of Total Harmonic Distortion (THD) to be very small and simultaneously improve the power factor. For comparison, the scenario used for the first experiment is a harmonic filter using only capacitor banks. The second scenario is mitigating harmonics by using several passive filters. While the third scenario uses both to mitigate harmonics or better known as Shunt Hybrid Power Filter (SHPF). The results of the scenario are scenario 3 with SHPF capacitor banks and some passive filters can mitigate harmonics best than other scenarios. Scenario 1 THD 10.98%, scenario 2 THD 4.69%, and scenario 3 THD 3.94%.

This article will show analysis effect pitch angles on vertical axis wind turbine (VAWT) on output power that can be generated by turbine. The background of this research is pitch angle will increase the thrust force of the wind turbine, so that the turbine rotation will be faster. Recalling that the turbine used is vertical turbine, then the turbine will be like spin. The method used in this article is calculate the turbine constant value (Cp). There are several parameters contained in the turbine constant values such as wind speed, turbine angular speed, tip speed ratio, turbine pitch angle. The value of the pitch angle will be varied from 00 until 400 to get the maximum turbine output power. The result, the pitch 300 angle can produce the highest power between the other angles. The highest power is 644.76 W at wind speed 20 m/s dan angular speed 32 rpm. Beside that, in low wind speed conditions, the pitch 00 can produce the highest power than other angels conditions, at 1 m / s power can be generated 0.787 W.

This paper introduces a novel intelligent computation adopted from a natural phenomenon entitled Thunderstorm Algorithm (TA) which is applied to optimize an economic dispatch under various technical constraints and environmental requirements. These studies used an IEEE-62 bus system as the sample model for demonstrating the ability of TA while searching the optimal solution. Obtained results show that TA gives good performances for determining the optimal solution. This demonstration also describes that the optimal solution is searched in faster convergence and shorter time. This computation also performs its characteristic in smooth and stable processes for completing all steps. In detail, the optimal solution is obtained in 22 steps for 15,586 $/h after pointing at 28,858 $/h at the first streaming. Based on executions, the computation needed 2.5 s for the streaming and it also needed 0.09 for covering the dead tracks included 0.5 s for the replacement.

Distributed Generation (DG) installation in the distribution system aimed to reduce power losses, increase the voltage profile, and increase the amount of power supplied. One DG that can be implemented is based on photovoltaic (PV). The stochastic PV characteristics will cause ups and downs of the power produced. When the power generated is greater than the load downstream, it will potentially cause the power flow to be two-way. The method for determining the placement location and optimal DG capacity is needed so that the effect of PV penetration on changes in power flow and voltage profile is still within the permissible limits. This study proposed the FPA algorithm to determine the optimal location and DG capacity to reduce power losses in the distribution system with a case study of distribution network in Semanu Substation, Gunung Kidul Yogyakarta. The findings confirmed that the FPA method can be used to determine the placement location and optimal DG capacity of 9.501 MW, with active power losses after DG having decreased by 68.75%, and reactive power losses of 61.40%.

The excitation system is a component which is used for DC power supply as a reinforcement of the synchronous generator, so that the generator can produce electric energy with the generator output voltage depending on the magnitude of current excitation. The stability of the voltage terminal or output voltage generator is strongly influenced by changes in current excitation. The generator is one type of electric machine is used as a means how to convert mechanical energy into electrical energy. In a generator, mechanical energy obtained from the first mover can be turbines, propellers and diesel engines. In the large power plants, one conversion tool that is often used is a three-phase synchronous generator. The synchronous generator is a synchronous generator with specification 1030 KVA, 420 V. Operation of the generator requires and stability so that the condition of the generator remains in optimal condition. The stability of the generator can be affected by namely loads, excitation current, power factor, and the round number generator. Change due to the voltage terminal connected to the load generator will cause instability. This study aims to look at examine changes electricity to the excitation current in the synchronous generator three-phase. From the analysis found that the increasing burden of the electro motive force (EMF) induction will also rise and the flow field where the EMF induction also increased in the can at the time of peak load power factor is lagging 481,68 Volt, excitation current 144,89 Ampere and the field current 510,52 Ampere, EMF Induction on leading power factor is 469,10 Volt, excitation current 136,65 Ampere and the field current 470,55 Ampere. The percentage value of voltage fluctuation in synchronous generator of the diesel power plant MIGAS during the observe reached between numbers - 4,30 % up to numbers -4,70 %. In the electricity supply system, the average electrical voltage has a varying supply point, that is according to national power company standard +5% and -10% but on the standard ANSI C 84.1 is preferred in +4% and -10%.

This study examined palm oil and diala B oil with variations in temperature changes between the two oils. Testing was conducted to obtain a minimum standard value of liquid isolator at the level of temperature variation for transformer oil. The results are compared with the standard liquid isolator to determine the feasibility of mixed oil as a liquid isolator. The purpose of this study is to determine the characteristics of palm oil as a substitute for mineral oil in its use as transformer oil. From the testing results, the value of oil breakdown voltage from 30° C to 100° C has the same increasing tendency, all oil samples meet IEC standard No.56 of 1991 which is above the standard at 60° C (standard ≥ 30kV/2, 5mm).

This paper describes the reduction in losses and improvement in reliability with transient and permanent outage of lines in transmission system. Each line switching is carried out for minimum loss results followed by load flow to get the objective of paper. All the buses are constrained by constant active power except the slack bus, so marginal losses under switching will supplied through slack bus. Power system component under repair considered as permanent outage and transient outages are for short duration. Switching rate for permanent outages will be minimum because it is replaceable but not in transient outage. Reliability is observable key for both the outages. Capacity Outage is used to evaluate the transmission system reliability parameter Expected Energy Not Supplied (EDNS) under load curtailments at the buses.

This paper presents steady-state performance comparison between standard boost converter and two-phase boost converter with reduced input current ripple characteristic. Several performance measurements were investigated under varying load conditions, which include output power, output voltage, input current ripple and efficiency. Results of the study using computer simulation reveal that the standard boost converter yielded input current ripple of 7.35% at full load while the two-phase converter draws a significantly less ripple of 0.01%. The difference, however, is not as pronounced on converter's efficiency at full load with the standard boost converter having 92.4% efficiency while the two-phase boost converter achieved 95.69% efficiency.

The purpose of this study was to analyze the effect of solar irradiance in the integration of photovoltaic plants (PV) on the stability of the electric power distribution system. The stability aspects assessed were the stability of the distribution network and the stability response of the synchronous generator which work in parallel with the PV generator. Distribution grid stability includes voltage and frequency stability. The stability response of synchronous generators includes power angle stability, active power stability, and reactive power stability. All aspects of stability were simulated based on four different cases of irradiance, namely 550 W/m2, 650 W/m2, 850 W/m2, and 1000 W/m2. PV generators were connected to a distribution grid using an inverter with MPPT and PV systems without batteries. This study was applied to a case of electrical distribution system in Gorontalo province, Indonesia. One distribution feeder in Gorontalo Province was connected a photovoltaic system of 2000 kWp and synchronous generators that work in parallel. The distribution system was modelled in a single-line diagram of 13-buses. The measurement of every solar radiation intensity change was conducted in every electrical distribution power system change. This study showed that high irradiation in PV plants affects the voltage and frequency stability on the grid by increasing proportionately. However, the high irradiation has an effect on increasing power oscillation at synchronous generators.

This paper describes the optimal design for the geometry of a stator slot for use in high-speed spindle motor applications. The proposed method consists of the following three steps: first, choose the parameters of the stator slot that has a strong influence on the stator current, stator winding loss, iron loss, total loss, efficiency, and torque by using the analysis of the effects of stator slot geometry; second, create factors and levels in the Taguchi method to obtain the optimal combination of the stator slot parameters from the analysis effect of the parameter results; third, using Genetic Algorithms (GAs) to determine the optimal value from the optimal combination of the results of the Taguchi method. Optimal design and performance analysis was performed using the Finite element Method (FEM) and verification by using equivalent circuit analysis. The optimization results were evaluated by comparing them with original performance. According to the test results and analysis, the optimal design of the stator slot geometry produce better performance than original design.

The electric power generation in electrical power system is very important in the process of distributing electrical energy to the load with the most optimal generated power and minimum generation costs. With the increase in electric power load requirements and generator fuel costs, economic dispatch is needed in a power generation system to obtain optimal and economic power generation. In this paper, researcher used an artificial bee colony algorithm that is one part of a swarm intelligence algorithms to get the best solution from optimization problems that is also widely used in other fields. The dynamic economic dispatch optimization of the 29 bus and 36 lines, 150 kV Sulselbar power generation system is carried out with consideration to generator power limits and generator ramp rate limits constraint. Simulation testing is done by comparing the simulation results from the same system using the Lagrange method which only considers generator power limit. The voltage stability in the system is also evaluated using L index stability and also in this paper is done loading margin on buses that were considered weak . The results of simulation show that artificial bee colony algorithm is able to provide the best solution of dynamic economic dispatch optimization

This paper discusses input-series output-parallel (ISOP) flyback-type micro-inverter which is implemented for ac photovoltaic module to achieved higher voltage input and output current rating. The main features of this micro inverter include a stable current injection, lower level harmonic distortion, potentially lightweight and lower cost. Additionally, as the inverter is mounted in a single PV module, the inverter may harvest maximum power when partial shading occurs. However, since the two flyback-inverters are connected in series/parallel, there should be control strategy for each inverter to obtain optimum performance. The strategy to control the inverters are based on a power sharing loop using droop method. Several experiments and simulations are carried out to examine the design and the results show the effectiveness of the proposed control strategy.

An examination is an important task in the education system to measure the level of students' understanding of teaching material. Replacing paper-based with an online system not only increases the efficiency of its process by reducing the consumption of papers, but also the amount of time spent marking and grading. However, a massive changing in exam operation happens on test participant. This research aims to reveal the user experience in the implementation of a website-based exam system. In particular, to study the first-time user experience including their attempt to understand how to use the application. The user experience is measured by User Experience Questionnaire (UEQ). 40 respondent participated in this research. All the respondent use the exam system for the first time without any prior training. According to the user responses, the first-time user rates the overall system in a good score in good and above average compared to the benchmark. However, the novelty and perspicuity dimension obtain the lowest score. Interviews confirm this value, some respondents put attention to the alternative of the interface in more modern technology such as mobile phone apps. The current interface also needs serious improvement such as font size, and question grouping

Pregnancy or fetal monitoring is an important medical examination of health issue. Hospital in a certain area is responsible for monitoring pregnant women to ensure their condition during pregnancy. Regular assessment in health facilities by a medical doctor has to be performed to measure some parameters, such as blood pressure, weight, baby movement, and other criteria. Nowadays, as mobile phone develops rapidly, it embeds many sensors. Those sensors help the medical practitioner to observe some of the parameters automatically. This research, therefore, proposes a mobile application to automatically record pregnant women mobility and the facility of image processing to read the weight of the scale. This paper aims to discuss the user experience in the implementation of the mobile smart birth monitoring application. The user's experience is measured by user experience questionnaire (UEQ) from 30 pregnant women in one of the districts in Central Java. According to the user responses, user experience shows that 5 out of 6 dimension of UEQ consider to be above average. Novelty and efficiency of the smart birth application need major improvement.

In recent years, digital wallet (e-wallet) took public attention as an alternative payment system in Indonesia. However, based on data from the World Bank, only 3% of the Indonesian population aged over 15 years old use digital wallet service in 2017. Therefore, to create a cashless society, efforts are needed to expand the use of digital wallet in the community. This research aims to identify the factors that influence the adoption of the digital wallet in Indonesia. We collected the data from 345 respondents that already use an e-wallet via an online survey. Then, we analyse the data using partial least square - structural equation modelling (PLS-SEM) based on UTAUT2 model with the addition of perceived risk and trust factor. This research supports that habit has the most substantial factor that influences the behavioural intention to adopt the digital wallet in Indonesia, followed by performance expectancy, trust, and facilitating conditions. However, effort expectancy, social influence, hedonic motivation, and perceived risk in the digital wallet adoption does not significantly affect the behavioural intention to use the digital wallet. The digital wallet stakeholder can use the result of this research as a suggestion to make a strategic decision related to digital wallet ecosystem.

In this paper, an educational software was developed using MATLAB and its Graphical User Interface (GUI). This interactive and user-friendly software focuses on three subjects which are electromagnetic, communication engineering and power engineering. The user uses MATLAB GUI in designing the educational software and then compiled together with M-file program to develop a standalone program. Standalone program was created to ease the students to use this educational software without installing MATLAB software in their personal computer. This software may help user to get a better understanding on electrical engineering subjects.

At present, the development of internet technology in Indonesia is very fast. The internet has been used in various fields, one of which is e-Learning in education. The application and implementation of e-Learning by each university are different. Today, a new e-learning model is being developed, namely MOOC. Generally, a new system requires readiness in its implementation so as not to fail. Based on previous research, the readiness of human resources has the lowest score, even though the readiness of human resources is one of the important factors in the implementation of e-Learning. The aim of this paper is the result of a preliminary study which outlines indicators of MOOC implementation readiness on human resources aspects. Classification, adoption, literature review, development analysis, and modification methods are carried out through papers published in international and national journals related to e-Learning readiness, human resources readiness and MOOC. The results of this study are components to measure the readiness of human resources, namely motivation, knowledge, experience, and skills. These indicators can be adopted into the university environment to measure the readiness of MOOC implementation on human resources aspects.

Electricity is one of the most essential infrastructures for economic development of any country. Since last 3-4 years, India is considered as one of the top five fastest growing economy in the world with an average annual growth rate of 7.3% to 7.6%. In F.Y 2019-20, India will continue its lead as the world's fastest-growing major economy as it intensify next year while the world economy is predicted to slow going. Agriculture based small-scale businesses play vital role in economic development of India. Conventional Jaggery manufacturing is one of the most popular small-scale business enterprises, which promotes local job employment and entrepreneurship opportunities in rural India. In F.Y 2018-19,India alone has supplied more than 75 % of worldwide jaggery demand, by exporting around 3 Lakh MT of jaggery, worth of Rs. 1500 crores. Nevertheless almost all jaggery units are located in remote places in rural India, where grid electricity is characterized by frequent power outages with poor quality of supply. Therefore, most of these jaggery units make use of diesel generators to power their loads, which are major contributor to global greenhouse gas (GHG) emissions. On the other side, India is bestowed with vast solar energy potential; receiving an average solar radiation in the range of 4-7 Kwh/m2/day, almost throughout the year in most parts of the country. Therefore, solar energy can be a possible alternative that can provide uninterrupted power supply to farmers and people involved in such agriculture based business enterprises. Our research team, surveyed several jaggery units from various parts of the country and did thorough study of problems faced by farmers while using Diesel Generator sets. Thereafter, our research team decided to provide technology up gradation to these rural business units. This research paper presents, technology up gradation of these conventional jaggery units and hence possible reduction in green house gas (GHG) emission using solar Photovoltaic system. Detailed performance analysis of the proposed system is done using PVsyst simulation tool.

Internet of Things (IoT) is increasingly developed at the present to connect the things to each other through the internet. Data transmission protocols play a significant role for devices and machine in an Internet of Things communications to exchange data without human intervention and knowing the singularity of one another. This paper introduces the literature works to analyze the existing papers based on data transmission of the protocols such as Message Queuing Telemetry (MQTT), Constrained Application Protocol (CoAP), Advanced Message Queuing Protocol (AMQP), Data Distribution Service (DDS) and Extensible Messaging and Present Protocol (XMPP). Furthermore we proposed the implementation model to evaluate the quality of the protocols such data transmission when operating the Machine to Machine communication, to support the application of Internet of Things based on real devices and detect the real environment condition in smart environment and monitoring system. The Challenge, method, result and conclusion of the perspectives are included in this paper to continuously improve of the system in the future works.

Quality of life in the family needs to be well managed. An example of one management is the need for electrical energy. Factors that can be managed by the family, namely home, health, electricity, and so on. Through this paper, researchers try to propose new methods that aim to manage the use of electricity. Managing electricity means regulating the use of electricity to be utilized properly so that it is not wasted. The method of using sensors integrated with the internet so that families can monitor and operate electricity use. The result is the integration between smartphones and internet-based electricity systems, making it easier to control the use of electricity. Savings payments from electricity use can be reduced by up to 38.5%.

This paper will explain the benefits and optimal use of smart home energy management systems viewed in various aspects. The background of this paper is many problems in Indonesia regarding the consumption of electricity and the depletion of natural resources. Although the government has applied for an energy-saving program, the implementation program is still not optimal and has not been able to overcome the existing problems. In this paper, the idea of applying the SHEMS (Smart Home Electrical Management System) will be divided into three parts including IoT control, manual / automation control, and monitoring of real-time electrical energy. This research will also analyze the effectiveness of SHEMS in controlling energy use from several sources of journal literature. There are several points related to journal analysis including the effectiveness of the existing method, multi-objective scheduling, SHEMS design implementation and comparison of the results of After and before using SHEMS. From the results of the journal literature analysis, it is expected to help find the right SHEMS design method for each different case and suggests a framework for future systems.

Internet of things (IoT) is one famous technology now. IoT capable objects will be interconnected through wired and wireless communication technologies. It makes demand high. One of solution using cognitive radio (CR) for better utilization, CR has spectrum sensing to find unoccupied channel dan occupied channel. This papar using Eigen Moment Ratio (EME) for better utilization than Energy Detection (ED). Evaluation show EME better than ED, because EME using new formula eigenvalue to detect. and EME can distinguish signal+noise from background noise only. EME also can detect using in multiantennas.

The government through the Directorate General of Higher Education of the Ministry of National Education seeks to allocate funds to provide tuition assistance to students who have high achievements or from poor families in the form of scholarships. However, due to a large number of students who apply to get a scholarship program so selection needs to be done. Therefore, the selection of recipients of the Academic Achievement Improvement (PPA) achievement scholarship was conducted with a case study in the Informatics Engineering Study Program STIMIK Sepuluh Nopember Jayapura using the AHP and TOPSIS methods. Some of the criteria used include GPA, Completeness of File, Parent Income, Number of Parent Dependents, and Job Status. This study concluded that the selection of scholarship acceptance was successfully carried out using a combination of AHP and TOPSIS methods. The test results using the User Acceptance Test with the number of questions 6 and the number of respondents 10 people where the average answer is 28.33% agree and 55% strongly agree. Selection of scholarship acceptance uses the AHP and TOPSIS methods in solving the problem of PPA scholarship acceptance by providing recommendations to the administration carried out by the Informatics Engineering study program effectively and efficiently in determining the recipients of the Academic Achievement Improvement Scholarship (PPA) scholarship

Many studies have addressed the importance of knowledge management on organizational outcomes such as organizational learning, innovation, product quality, besides creative, financial, economic and organizational performance. BPK RI, like other organizations in the world, is relied more and more heavily to IT to do its primary job (audit) and in its daily routine. With rapid development in IT in recent years, BPK RI needs to make sure it has competent IT staffs. One competency, among other, needed is IT troubleshooting. IT troubleshooting is important because it can determined the whole operations of BPK RI. Faster response and faster problem-solving will make sure BPK RI can do business as usual. To address this problem, BPK RI has initiated to implement a knowledge management system. The result of this research is KMS design for IT troubleshooting. It covers the externalization, socialization for knowledge sharing, exchange, routines, and directions processes that is deemed important by BPK RI

BPS mission is to provide high quality statistical data through an integrated statistics by implementing national and international standards. To enables practical application of standard statistical units and their classifications, BPS initiated create Master Data to integrated source of frame for all economic survey, that's called statistical business registers (SBR). Main objective of SBR become the primary and only establishment and enterprise directory. But, the current situation, several directories of establishments exist, maintained by various SMEs. Therefore, assessment to SBR in BPS is important, because it will be a benchmark for the success of SBR in BPS. The measurement was conduct using Master Data Management Maturity Model (MD3M), by providing questionnaires for the two SMEs at BPS, filled with interview. From the domains assessed, the result shows that the maturity level rate of SBR is at level 1 on a score from 1 to 5. This means that a first awareness for issues regarding the topic of MDM has been raised on an operational level. Initial steps are initialized.

Vehicle Routing Problem (VRP) is one of the combinatoric problems that is difficult to solve, so it is incorporated into NP hard problem. VRP aims to produce a set of shortest routes from several of the same capacity vehicles to visit several customers at a certain time limit. Depot is the starting and ending point of the route. Departing from the increasingly complex industry needs, the VRP problem needs to be developed into a multi-objective. Most of the previous VRP studies, only using single objectives, were minimizing total distance. Therefore, in this study added an objective related to the balance of distances between routes. In previous studies, multi-objective VRP was completed using metaheuristic. The method requires the determination of parameters and a special algorithm design for each problem to be solved. To overcome these shortcomings, this research uses a hyper-heuristic method to complete multi-objective VRP. Given that the use of hyper-heuristics in previous studies is for single objective VRP, so this study also propose hyper-heuristic for multiobjective VRP. Gehring and Homberger dataset is used for experiment. Based on the experiments in this study, Hill Climbing gives better results than Great Deluge for completing multiobjective VRP.

All businesses focus on profit. Therefore, the use of costs in the company needs to be managed as efficiently as possible. For this reason, companies need a system that can measure the benefits of cost usage. Generally, employees in small companies do tasks outside their expertise to cope with the demand. Especially in small software companies with Scrum methodology where the team is encouraged to participate in backlog completion regardless of their competency level. The cost implication of the task being done by non-expert team member can be higher than when the company hires more employee with specific expertise. The addition of Activity Based Costing model to management tools can help the company in detailing the resource's activities and cost. The model can also be used to analyze the activity value to confirm the benefits. The design validation shows that the system can be used more than just cost measurement, but also measuring resource usage, and indicating employee's competencies for strategic planning.

In the development of higher education is very much determined by the rule of information technology. STIMIK Sepuluh Nopember Jayapura has a problem that institutions tend not to realize the importance of managing or improving governance and transparency in institutions. One very important process in improving Information Technology governance is conducting a useful evaluation to determine the level of maturity IT implementation in the institution. This research explains the procedures of universities in improving governance and transparency. The Balanced Scorecard is a good framework for assessing Organizational performance. Collaboration between the COBIT4.1 Framework and the Balanced Scorecard can provide a guide to financial perspectives that can be used as guidance by stakeholders in improving governance and transparency. This study also obtained a way to improve maturity levels through recommendations that can be used as guidance by stakeholders in developing IT governance that is in accordance with COBIT best practices 4.1. In this study concluded that the maturity level of institutional governance is currently at level 3 which has an average value of 2.80 with categories defined