Implementation of Data Mining for Classifying Student Graduation Levels Using Naive Bayes Decision Tree Random Forest Support Vector Machines and Neural Networks Methods Case Study of The Undergraduate Program at Mitra Indonesia University

Abstract

This study aims to classify student graduation levels using five data mining methods: Naive Bayes, Decision Tree, Random Forest, Support Vector Machines, and Neural Networks. Conducted as a case study at Mitra Indonesia University, the research utilizes academic data, including GPA, course completion rates, and attendance records, to predict graduation success. The results reveal that Random Forest and Neural Networks exhibit the highest accuracy, making them the most suitable methods for predicting student outcomes. These findings contribute to the development of early intervention programs for students at risk of delayed graduation, providing valuable insights for higher education institutions.