Performance Graduation Student Predicting Using One-Class Support Vector Machine Algorithm

Abstrak

This study explores the prediction of student graduation performance using the One-Class Support Vector Machine (OCSVM) algorithm. The objective is to accurately forecast the time and success rate of students graduating from academic programs. Predicting student performance has become increasingly vital for educational institutions aiming to improve retention rates and support academic planning. The research employs the OCSVM due to its effectiveness in handling imbalanced datasets, which are common in academic performance data. By focusing on a single class, the algorithm can detect anomalies and patterns that signify potential delays or failures in graduation. The dataset comprises various academic and demographic attributes of students from a private university in Indonesia. Data preprocessing techniques such as normalization and transformation were applied to enhance the model's accuracy. The results demonstrate that the OCSVM algorithm can effectively predict student graduation performance with a high degree of accuracy, offering educational institutions a robust tool for early intervention. This approach not only helps in identifying at-risk students but also facilitates the development of targeted support strategies, thereby enhancing overall academic outcomes.