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All Journal JUITA : Jurnal Informatika Tech-E bit-Tech JUSTIN (Jurnal Sistem dan Teknologi Informasi) eCo-Fin Abdi-Dharma SATIN - Sains dan Teknologi Informasi
Ardiane Rossi Kurniawan Maranto
Universitas Buddhi Dharma
Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Analisis Komparatif Algoritma Pembelajaran Mesin untuk Mengidentifikasi Keterlambatan Kelulusan Mahasiswa Benny Daniawan; Suwitno Suwitno; Andri Wijaya; Ardiane Rossi Kurniawan Maranto; Junaedi Junaedi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30186

Abstract

Delayed student graduation is a critical issue in higher education because it affects academic planning, student support, and institutional performance evaluation. This study develops a leakage controlled machine learning framework for early identification of students at risk of delayed graduation, using academic records available through the sixth semester. A dataset of 564 students was used, with graduation status defined as on-time for students graduating in the eighth semester or earlier and delayed for those graduating after the eighth semester. To prevent temporal data leakage, post-outcome variables were excluded from the predictor set. Five supervised learning algorithms were evaluated: Decision Tree, Support Vector Machine, Random Forest, Naïve Bayes, and K-Nearest Neighbor. Preprocessing was performed using one-hot encoding and standardization within a pipeline, and model performance was assessed using stratified five-fold cross-validation. The tuned Random Forest achieved the most balanced performance, with 0.956 accuracy, 0.861 delayed-class precision, 0.805 delayed-class recall, 0.832 delayed-class F1-score, and 0.977 ROC-AUC. The tuned SVM with a threshold of 0.30 achieved higher delayed-class recall (0.857) and ROC-AUC (0.980). Feature-importance analysis indicated that fourth and fifth semester GPAs were the strongest predictors. These findings show that machine learning can support early academic intervention and data driven decision making in higher education.
Co-Authors Aditiya Hermawan Alvin Rahayu Andi Leo Andri Wijaya Benny Daniawan Fernando Fanjaya Hari Soetanto Harisa Mardiana Intan Anjali Putri Irvan Rahul Ramadika Junaedi Jupiter Supriyadi Leonardo Lianata Liliy Damayanti Maysha Permata Putri Nicolas, Prayogi Perdana Riki Riki Suwitno Suwitno Suwitno Wahyudi Wahyudi Yuliastati Putri Sugiarta Karlim