A Study on Student Graduation Classification at Nahdlatul Ulama University of Lampung Using the Naive Bayes Method

Abstract

This study aims to apply a classification model using the Naive Bayes method to analyze the factors influencing student graduation at Nahdlatul Ulama University of Lampung (Unula). The Naive Bayes method was chosen for its ability to handle high-dimensional data and provide efficient classification results. The data used in this study were derived from students' academic records, including course grades, attendance, and participation in extracurricular activities. The classification process involved splitting the dataset into training and testing sets to evaluate the model's accuracy. The results show that the Naive Bayes model can classify student graduation outcomes with a satisfactory level of accuracy. Furthermore, the analysis identified significant factors contributing to graduation, such as average grades and attendance rates. These findings are expected to provide insights for the university in designing strategies to improve educational quality and support students in achieving timely graduation. This research also opens opportunities for further studies on the application of other classification methods in academic analysis within higher education settings.