Implementation of Naïve Bayes for Classification of Learning Types

Learning is a process that is carried out by each individual from not knowing to knowing, or from bad behavior to being good, so that it has a good change for the individual, Each individual has a learning type in receiving the material presented by the teacher, but not all individuals understand what type of learning they need, The purpose of the research is to determine the type of learning of the students of the Faculty of Computer Science. The method used is nave Bayes for the accuracy of its calculations. The results of this study are the classification of visual learning types as many as 50 people, for audio as many as 24 people, while kinesthetic as many as 25 people, for the Informatics Engineering Study Program as many as 61, consists of 37 visual learning types, Auditory 14 people, Kinesthetic 10 people, While the Information Systems Study Program is 37 people, where is Visual 14 people, Auditory 9 people and Kinesthetic 14 people. With this classification, it can help lecturers apply learning methods that are suitable for their students. The best Naïve Bayes accuracy rate is 88.89%