Optimasi Hyperparameter XGBoost Berbasis Bayesian Optimization dan SHAP untuk Prediksi Dropout Mahasiswa Perguruan Tinggi
DOI:
https://doi.org/10.30865/jurikom.v13i4.10055Keywords:
Bayesian Optimization, Dropout Mahasiswa, Explainable AI, SHAP, XGBoostAbstract
Putus studi (dropout) mahasiswa merupakan masalah pendidikan tinggi yang merugikan secara finansial dan sosial, terutama karena proporsi kelas dropout yang tidak seimbang serta minimnya model prediksi yang transparan dan teruji stabil. Penelitian ini menerapkan algoritma Extreme Gradient Boosting (XGBoost) yang hyperparameter-nya dioptimasi melalui Bayesian Optimization berbasis Gaussian Process sebanyak 50 iterasi, dilengkapi ambang keputusan berbasis skor F2, yaitu metrik yang memberi bobot lebih besar pada recall dibandingkan precision, untuk mengatasi ketidakseimbangan kelas, serta interpretasi Shapley Additive exPlanations (SHAP) guna menjelaskan kontribusi tiap fitur terhadap prediksi model. Penelitian ini bertujuan mengembangkan kerangka kerja prediksi dropout yang akurat, transparan, dan tervalidasi secara statistik pada dataset 4.424 mahasiswa dengan 34 atribut akademik, demografis, dan sosioekonomi. Hasil sementara menunjukkan bahwa model XGBoost teroptimasi mencapai luas area di bawah kurva Receiver Operating Characteristic (ROC-AUC) sebesar 0,930 dan luas area di bawah kurva presisi-recall (PR-AUC) sebesar 0,904 pada data uji, dengan recall 90,49% pada ambang F2 optimal sebesar 0,2251. Validasi tambahan melalui nested cross-validation menghasilkan ROC-AUC rata-rata 0,924, yang mengonfirmasi stabilitas performa model. Interpretasi SHAP mengungkapkan bahwa jumlah mata kuliah yang lulus pada semester kedua merupakan prediktor paling dominan terhadap risiko dropout. Temuan ini menegaskan bahwa integrasi optimasi hyperparameter dan explainable AI mampu menghasilkan model prediksi dropout yang andal dan akuntabel bagi kebijakan intervensi akademik dini.
References
[1] D. A. Radulescu and V. E. Balas, “Explainable Early Warning Systems for Student Dropout Prediction: Insights from a Bibliometric Analysis,” International Journal of Computers, Communications and Control, vol. 21, no. 4, Jul. 2026, doi: 10.15837/ijccc.2026.4.7599.
[2] J. Niyogisubizo, L. Liao, E. Nziyumva, E. Murwanashyaka, and P. C. Nshimyumukiza, “Predicting student’s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization,” Computers and Education: Artificial Intelligence, vol. 3, Jan. 2022, doi: 10.1016/j.caeai.2022.100066.
[3] S. A. Tazehkand and M. C. Wang, “Leveraging XGBoost to Reduce Failure, Withdrawal, and Dropout Rates in Undergraduate Level Mathematics/Statistics Education,” in Frontiers in Artificial Intelligence and Applications, IOS Press BV, Jul. 2024, pp. 396–404. doi: 10.3233/FAIA240276.
[4] L. G. R. Putra, D. D. Prasetya, and M. Mayadi, “Student Dropout Prediction Using Random Forest and XGBoost Method,” INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, vol. 9, no. 1, pp. 147–157, Feb. 2025, doi: 10.29407/intensif.v9i1.21191.
[5] S. Kim, E. Yoo, and S. Kim, “Why Do Students Drop Out? University Dropout Prediction and Associated Factor Analysis Using Machine Learning Techniques,” Oct. 2023, [Online]. Available: http://arxiv.org/abs/2310.10987
[6] O. Goren, L. Cohen, and A. Rubinstein, “Early Prediction of Student Dropout in Higher Education using Machine Learning Models,” in Proceedings of the International Conference on Educational Data Mining, International Educational Data Mining Society, 2024, pp. 349–359. doi: 10.5281/zenodo.12729834.
[7] Z. Song, S. H. Sung, D. M. Park, and B. K. Park, “All-Year Dropout Prediction Modeling and Analysis for University Students,” Applied Sciences (Switzerland), vol. 13, no. 2, Jan. 2023, doi: 10.3390/app13021143.
[8] M. Mihoubi, M. Zerkouk, and B. Chikhaoui, “Beyond classical and contemporary models: a transformative AI framework for student dropout prediction in distance learning using RAG, Prompt engineering, and Cross-modal fusion,” Jul. 2025, doi: 10.21125/edulearn.2025.0815.
[9] K. F. B. Soppe, A. Bagheri, S. Nadi, I. G. Klugkist, T. Wubbels, and L. D. N. V Wijngaards-De Meij, “Predicting First-Year Dropout from Pre-Enrolment Motivation Statements Using Text Mining.”
[10] M. Faris Al Hakim et al., “Optimization of Machine Learning Model using Grid and Random Search Algorithms for Predicting Student Dropout,” Jurnal Teknik Informatika (JUTIF), vol. 7, no. 3, 2026, doi: 10.52436/1.jutif.2026.7.3.5627.
[11] M. Nagy and R. Molontay, “Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention,” Int. J. Artif. Intell. Educ., vol. 34, no. 2, pp. 274–300, Jun. 2024, doi: 10.1007/s40593-023-00331-8.
[12] I. K. Nti and S. Ramanayake, “Explainable machine learning for student dropout prediction and tailored interventions in online personalized education,” Discover Artificial Intelligence, vol. 6, no. 1, Dec. 2026, doi: 10.1007/s44163-026-01016-6.
[13] R. A. M. Al Hashmi, P. D. Zervopoulos, H. M. Elmehdi, and I. Ozturk, “Predicting Dropout in MENA STEM Higher Education Using Explainable AI: A Machine Learning Approach,” Emerging Science Journal, vol. 9, no. Special Issue, pp. 268–286, May 2025, doi: 10.28991/ESJ-2025-SIED1-016.
