Machine learning is computer programming that uses a computational approach to optimize a criterion or parameter from a dataset or past data to determine performance and patterns. The targeted object can come from physical and non-physical things. The start of the initial successful computing machine created in 1946 changed the human paradigm in the use of technology. The computing machine is known as the first computer, ENIAC, which performed numerical computations capable of solving numerical problems. Although simple, the development of this machine was quite significant. In 1950, Alan Turing proposed a Turing test. Testing the interaction of humans and machines, whether machines can think like humans. This proposal did not stop there. In 1952, the first game program for checkers by embedding a knowledge base system on the machine welcomed this proposal. Various methods developed quite rapidly and continued. In 1957, the perceptron method emerged and was designed with several models to form a neural network. In the early 90s, statistical learning theory was added to the learning method for processing data by placing rule-based inference. Until now, the development and approach of the artificial intelligent model have been made drastically so that the military, industry, and all sectors can take full advantage of it. The input data becomes the main thing to become the object of observation so that it becomes information and experience becomes a valuable knowledge of a model. Suppose a data is assumed to represent a series of vectors of each dimensionality, where the vector is a set of numbers. It implies that each vector is a data point, and each dimension is a feature. Supporting devices from hardware and software are pretty massively developed. The development methods accompany these technologies, e.g., various stand-alone methods, method combinations, randomization methods, parameter tuning with an evaluation of its complexity, pre-processing of data, and feature selection which can separate data from noisy features.

This conference took the theme of machine learning for data science with a background in rapidly developing applications and methods in the data processing. Various approaches in optimization and modeling make building construction in the world of artificial intelligence get a higher place. Raw data is processed where it used to take a relatively long time to become information, and there is even data that is not utilized at all. Still, now it is a valuable material for processing. Current data trends trigger a flood of data from all aspects and dimensions of life. Big data becomes easy to process and display only numerical and graphical forms. Prediction by utilizing a data processor is quite capable of analyzing the future. However, not precisely, but it is pretty helpful to mitigate an event that will come. Information on growth in the 2000s increased by around 30%, and the estimate exceeds that percentage. Researchers and observers need knowledge discovery to make data display rational and its use reasonably practical. Hopefully, the papers in this conference have a meaningful contribution to life with various dimensions of its busyness during this pandemic. Although this conference is held online due to regulatory and transportation limitations, the power of innovation and creativity must always grow.

Editorial Boards,

Widyastuti Andriyani
Ferry Wahyu Wibowo