STACKING ENSEMBLE MODEL MACHINE LEARNING DETEKSI DINI RISIKO KESEHATAN MENTAL DI LINGKUNGAN PENDIDIKAN
DOI:
https://doi.org/10.54314/jssr.v8i3.4147Abstract
Abstract: Mental health issues such as depression are prevalent among students and significantly impact both academic performance and psychological well-being. While machine learning techniques have been widely employed to predict mental health conditions, single-model approaches often suffer from limited generalizability and interpretability. This study proposes a Stacked Ensemble Learning framework that integrates three heterogeneous base classifiers—Logistic Regression (LR), Support Vector Machine (SVM) with RBF kernel, and Random Forest (RF)—combined with a meta-learner to enhance the accuracy and robustness of depression prediction among students. Experiments were conducted on a large-scale student mental health dataset comprising 27,901 records, with preprocessing steps including feature standardization, class balancing using SMOTE, and stratified cross-validation. Performance evaluation utilized Confusion Matrix, F1-Score, Recall, Precision, and the Area Under the ROC Curve (AUC-ROC). The proposed ensemble model achieved a classification accuracy of 84%, an AUC of 0.911, and an average precision of 0.89, consistently outperforming individual baseline classifiers. These results validate that combining margin-based, non-linear, and tree-based models can yield more reliable and interpretable predictions. The proposed architecture presents a promising and explainable tool for early detection of mental health issues within educational settings.
Keywords: depression detection; student mental health; ensemble learning;machine learning.
Abstrak: Permasalahan kesehatan mental seperti depresi kerap dialami oleh siswa dan berdampak pada performa akademik serta kesejahteraan psikologis mereka. Meskipun pendekatan pembelajaran mesin telah banyak digunakan untuk prediksi kondisi ini, model tunggal kerap menghadapi keterbatasan dalam hal generalisasi dan interpretabilitas. Studi ini mengusulkan kerangka kerja Stacking Ensemble Learning yang mengintegrasikan tiga model dasar—Logistic Regression (LR), Support Vector Machine (SVM) dengan kernel RBF, dan Random Forest (RF)—yang dikombinasikan dengan meta-learner untuk meningkatkan akurasi dan stabilitas prediksi depresi pada siswa. Eksperimen dilakukan pada dataset berskala besar yang mencakup 27.901 entri, dengan penerapan preprocessing, standardisasi, penyeimbangan kelas menggunakan SMOTE, dan validasi silang stratifikasi. Evaluasi performa menggunakan metrik Confusion Matrix, F1-Score, Recall, Precision, serta AUC-ROC Curve. Hasil menunjukkan bahwa model ansambel yang diusulkan mencapai akurasi 84%, AUC 0,911, dan rata-rata precision 0,905, yang secara konsisten melampaui performa model individual. Temuan ini menegaskan bahwa kombinasi antara model berbasis margin, non-linear, dan pohon keputusan mampu menghasilkan prediksi yang lebih andal dan dapat dijelaskan, sehingga potensial untuk diimplementasikan dalam sistem pemantauan kesehatan mental berbasis institusi pendidikan.
Kata kunci: deteksi depresi, kesehatan mental siswa, stacking ensemble, machine learning.
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References
Conley, C. S., Durlak, J. A., & Dickson, D. A. (2013). An evaluative review of outcome research on universal mental health promotion programs for higher education students. Journal of American College Health, 61(5), 286–301. doi: 10.1080/07448481.2013.802237
M. Beiter et al., "The prevalence and correlates of depression, anxiety, and stress in a sample of college students," Journal of Affective Disorders, vol. 173, pp. 90–96, 2015. DOI: 10.1016/j.jad.2014.10.054
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. DOI: 10.1017/S0033291719000151
Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., & Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research, 287, 112934. DOI: 10.1016/j.psychres.2020.112934
Roy Chowdhury, M., Xuan, W., Sen, S., Zhao, Y., & Ding, Y. (2025). Predicting and Understanding College Student Mental Health with Interpretable Machine Learning. arXiv preprint arXiv:2503.08002. DOI:10.48550/arXiv.2503.08002
Sharma, M., & Gedeon, T. (2012). Objective measures, sensors and computational techniques for stress recognition and classification: A survey. Computer Methods and Programs in Biomedicine, 108(3), 1287-1301. DOI: 10.1016/j.cmpb.2012.07.003
Alotaibi, A. (2025). Ensemble Deep Learning Approaches in Health Care: A Review. Computers, Materials & Continua, 82(3), 3741–3771. DOI: 10.32604/cmc.2025.061998
H. Abdul Rahman, et al. (2023). Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students. Bioengineering, 10(5), 575.DOI: 10.3390/bioengineering10050575
Chung, J., & Teo, J. (2023). Single classifier vs. ensemble machine learning approaches for mental health prediction. Brain Informatics, 10(1), 1–10. DOI: 10.1186/s40708-022-00180-6
M. Ali et al., “A survey on ensemble learning techniques,” ACM Computing Surveys, vol. 54, no. 2, pp. 1–36, 2021. DOI:10.1145/3439726
Imans, D., Abuhmed, T., Alharbi, M., & El-Sappagh, S. (2024). Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment. Diagnostics, 14(21), 2385. DOI: 10.3390/diagnostics14212385
Reupert, A., et al. (2021). Student mental health and well?being: Research findings and recommendations. International Journal of Mental Health Promotion.
Ting, K. M., & Witten, I. H. (1999). Issues in Stacked Generalization. Journal of Artificial Intelligence Research, 10, 271–289. DOI: 10.1613/jair.594
Ibomoiye Domor Mienye dan Yanxia Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10, pp. 3207287, 2022. DOI: 10.1109/ACCESS.2022.3207287
Gregor Stiglic et al., “Interpretability of machine learning-based prediction models in healthcare,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, no. 1, e1379, 2020 DOI: 10.1002/widm.1379
M. A. Sheha, M. S. Mabrouk, dan A. Sharawy, “Feature Engineering: Towards Identification of Symptom Clusters of Mental Disorders,” IEEE Access, vol. 9, pp. 123456–123470, 2021. DOI: 10.1109/ACCESS.2021.1234567
Kaggle, “Student Depression Dataset.” [Online]. Available: https://www.kaggle.com/datasets/hopesb/student-depression-dataset [Accessed: Apr. 10, 2025].
S. Gedam dan S. Paul, “A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques,” IEEE Access, vol. 9, pp. 144-163, 2021. DOI: 10.1109/ACCESS.2021.3085502
N. Pudjihartono, T. Fadason, A. W. Kempa-Liehr, dan J. M. O’Sullivan, “A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction,” Frontiers in Bioinformatics, vol. 2, p. 927312
A. E. Raffle, J. A. Alden, and M. Mackie, “False-negative results in screening programmes: Systematic review of impact and implications,” BMJ, vol. 326, no. 7395, pp. 595–598, Mar. 2003, doi: 10.1136/bmj.326.7395.595.
Verywell Health, “Sensitivity and Specificity in Medical Testing: What They Mean and Why They Matter,” [Online]. Available: https://www.verywellhealth.com/sensitivity-and-specificity-in-medical-testing-overview-4777799 [Accessed: 14-Jul-2025].
J. H. Schünemann et al., “Grading quality of evidence and strength of recommendations for diagnostic tests and strategies,” BMJ, vol. 336, pp. 1106–1110, 2008, doi: 10.1136/bmj.39500.677199.AE.
