Optimization of Machine Learning Models in Student Graduation Prediction Systems Using Ensemble Learning with PSO and SMOTE

Abstract

The timely graduation of students is a key metric in evaluating the academic effectiveness of higher education institutions. However, accurately identifying students at risk of delayed graduation remains challenging due to imbalanced data distributions and the instability of single-model prediction approaches. This study proposes an optimized ensemble-based machine learning system for predicting on-time graduation among university students. The model integrates C4.5, K-Nearest Neighbor (KNN), and Random Forest algorithms through a hard voting classifier, which is further optimized using Particle Swarm Optimization (PSO) to determine the most effective weighting configuration. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is implemented, ensuring balanced representation between timely and delayed graduates. A dataset of 809 student academic records from Universitas Sains dan Teknologi Indonesia (USTI) was used, and performance was evaluated using 5-fold cross-validation. The proposed ensemble model achieved an average accuracy of 93.70%, a precision of 0.94, a recall of 0.93, and an F1-score of 0.94, outperforming each individual classifier. These results confirm that the combination of ensemble learning, PSO-based optimization, and data balancing effectively improves both accuracy and model stability. The findings highlight the system’s potential as a reliable decision-support tool for educational institutions to anticipate delayed graduations and improve academic supervision strategies.

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