The Decision Tree C5.0 Classification Algorithm for Predicting Student Academic Performance - Natanael Benediktus; Raymond Sunardi Oetama (Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, 2020)
Student’s performance is often used as a benchmark and a student’s activeness is frequently used as a criteria of how well a student academically perform at school. Where in this study would try to find out whether the activeness of a student can predict their academic performance. The data used is an educational dataset is collected using a learning management system (LMS), which is a learner activity tracker tool that is connected by the internet. This data has numerical and categorical variables, so it is needed to have the right algorithm to classify data accurately and ensure data validity. In this study, the C.50 algorithm is used to test the data, where the data is divided into training data by 75% and testing data by 25%. And the result from the tested data, an accuracy of 71.667% is obtained.