Prediction of On-Time Graduation of Students Using Random Forest Algorithm (Case Study: Faculty of Computer Science, Universitas Brawijaya)

The timeliness of student graduation is an important indicator of academic quality and institutional performance. Delayed graduation not only affects university evaluation metrics but also postpones students’ entry into the workforce. This study proposes a predictive model to identify students at risk of delayed graduation at the Faculty of Computer Science, Universitas Brawijaya. A comparative evaluation of three classification algorithms, namely Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), was conducted within a Knowledge Discovery in Databases (KDD) framework. SMOTE was applied to address class imbalance, while Stratified K-Fold Cross-Validation was used to ensure robust model assessment. Experimental results show that the Random Forest model achieves the best performance, with an accuracy of 73% and an AUC of 0.79, outperforming SVM and KNN. Feature importance analysis further indicates that Grade Point Average, particularly in the third semester, is a more influential predictor of on-time and delayed graduation than credit accumulation. These results demonstrate the potential of the proposed model as an early warning system for proactive academic intervention.