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Priaji Januardi
Universitas Amikom Purwokerto
Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Audit Data Leakage dan Evaluasi Model Machine Learning untuk Prediksi Capaian Pembelajaran Lulusan dalam Outcome-Based Education Priaji Januardi; Rujianto Eko Saputro; Fandy Setyo Utomo
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Outcome-Based Education (OBE) implementation requires continuous evaluation of graduate learning outcomes (CPL). While machine learning models are widely used for predicting academic performance, most studies overlook fundamental issues like data leakage and class imbalance. This study evaluates the impact of data leakage audits on the performance of four models (Logistic Regression, Random Forest, XGBoost, and Deep Neural Network) in predicting CPL. The novelty lies in applying a structured audit framework prior to model comparison. Experiments utilized 27,262 academic records with stratified cross-validation. Audit results proved that proxy features caused unrealistic performance (Accuracy 0.999). After removing leaked features, Logistic Regression achieved the highest discrimination stability (AUC 0.772), while DNN recorded the highest F1-score. Wilcoxon tests confirmed no statistically significant performance difference among the models (α=0.05). In conclusion, on leakage-free OBE data, simple linear models remain highly competitive and suitable as the foundation for early warning systems.
Co-Authors Fandy Setyo Utomo Rujianto Eko Saputro