Feature selection methods in improving accuracy of classifying students' academic performance

Abstract

Data mining began to be applied in various fields, one of them on educational data. By exploring information or knowledge in a data allows an institution to improve the learning process and the quality of the institution. This research proposes feature selection techniques in improving Student's Academic Performance classification accuracy. The algorithm used is Naive Bayes, Decision Tree, and Artificial Neural Network, which will be applied to the features selection; wrapper and information gain. The application of feature selection is intended to obtain a higher accuracy value. When compared to the embedded method in previous studies, the feature selection on this experiment has a lower accuracy rate.