Analyzing data has a long history. Many terms have been widely used to describe this field of data analysis, including statistics, artificial intelligence, machine learning, and data analytics. This study is also known as data science. The most fundamental thing in this study are experiments, observations, and numerical simulations in all fields of science and business that use big data from terabytes of data, and in some cases, up to petabytes and above. This information analysis consists of datasets from various fields from knowledge on the earth's surface, social processes, information-based industries, and outer space. The knowledge that has a lot of data causes choices and considerations for calculations in exact theory to be ignored even though data science is a value approach with a fast way to get precise values.

The development of data science includes all data without exception so that data that was previously only stored in files and folders can now be used to dig up information. Data retrieval capabilities can include understanding data, processing data, extracting values, visualizing it, and communicating. These abilities are essential in the coming periods for professionals and ordinary people who can learn them. It is because there is a lot of data everywhere. Meanwhile, these critical properties will bring up ideas in data processing. Five main things make up big data: volume, velocity, variety, value, and veracity. The volume sector means that data is collected over time so that it becomes many from terabytes, petabytes, to exabytes. Many ideas arise when the computer can't process data, such as MapReduce, Hadoop, etc. The velocity sector causes data to be generated very quickly.

In contrast, the data variety sector has differences in its types, so selecting and comparing it requires carefulness and accuracy. Sector value data must have good considerations so that to be able to understand the data needs to be converted into a specific value. In the veracity sector, the data to be processed must be flawless and appropriate. However, data science can be done on small datasets. Otherwise, not everything done in big data will be referred to as data science. So there are things that overlap in the two terms. In real-world problems, it can be said that there is no definite provision of a data inference. What is unacceptable is when the data do not support the reported conclusions.

Given the many implementations and applications of this data processing, the 2021 International Seminar on Machine Learning, Optimization, and Data Science (2021 ISMODE) appears. We hope that this 2021 ISMODE will start to advance the exchange of ideas, concepts, and engineering in the data processing. Even though it is still in a COVID-19 pandemic, this activity must be in line with the development of existing technology so that it does not stagnate in efforts to develop knowledge.

Editorial Boards,

Dr. Eng. Irwan Prasetyo, MPM
Ali Khumaidi, S.Kom, M.Kom
Ferry Wahyu Wibowo