Student Learning Style Classification Using Naive Bayes Classifier Method - Ramadandi, S; Jahring, Jahring (Program Studi Sistem Informasi, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia., 2020)
Every student has their own habits in absorbing and processing lecture material provided. This habit is called learning style. Knowing student learning styles is very important for a lecturer because by knowing students' learning styles in one class, lecturers can apply learning methods that can accommodate all student learning styles. In the Computer course in the Indonesian Language Education Study Program and the English Language Education Study Program, there are still some students who have difficulty understanding lecture material because the learning methods given by the lecturer are only fixated on certain learning styles. For this reason, this research will help lecturers to determine student learning styles based on previous data using the NaÃ¯ve Bayes Classifier method in Data Mining. Some studies suggest that the NaÃ¯ve Bayes Classifier method is better than other classification methods. In this study, researchers used Rapid Miner as a tool for classification. After testing the test data, an accuracy value of 90% is obtained. This proves that the classification model formed from training data can provide a good classification of learning styles and this model can be applied by lecturers to determine student learning styles.