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All Journal Riau Journal of Computer Science JURNAL INSTEK (Informatika Sains dan Teknologi) JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Building of Informatics, Technology and Science Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) Jutsi: Jurnal Teknologi dan Sistem Informasi J-Com (Journal of Computer) Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo Jurnal Bangun Abdimas Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Journal Of Indonesian Social Society Jurnal Pengabdian Harapan Bangsa Jurnal Teknologi Ilmu Komputer Jurnal Ilmiah Multidisiplin Nusantara The Indonesian Journal of Computer Science
Rolly Yesputra
Sekolah Tinggi Manajemen Informatika Dan Komputer Royal
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STACKING ENSEMBLE MODEL MACHINE LEARNING DETEKSI DINI RISIKO KESEHATAN MENTAL DI LINGKUNGAN PENDIDIKAN Putri, Lia Umbari; Yesputra, Rolly; Prayogi, Satria Yudha; Marpaung, Nasrun
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4147

Abstract

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 classifiersLogistic 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 dasarLogistic 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.
Co-Authors Abda Abda Adela Fitri Afdhal Syafinur Afdhal Syafnur afrisawati, Afrisawati Ahmad Ariandi Andri Nata Andy Sapta Ariandi, Ahmad Arridha Zikra Syah Awanda Sari, Windi Chan, Zulfira Elisa Dalimunthe, Roni Dewi Maharani Edi Kurniawan Endra Saputra Fifi Safitri Fitri, Adela Gultom, Karyawaty Guntur Maha Putra Hanafi Asnan Ibnu Mardhotillah Indah Purnama Sari Jeperson Hutahaean Jhonson Efendi Hutagalung Karyawaty Gultom Laina Tussifah Lia Umbari Putri Lia, Lia Umbari Putri M Rizky Pratama Marpaung, Nasrun Masri Wahyuni Muhammad Amin Nasrun Marpaung Nasution, Akmal Novita Pasha Nur Adha Rini Nuriadi Manurung Putri, Lia Umbari Rika Nofitri Risnawati Risnawati Risnawati Risnawati Rohminatin Rohminatin Rohminatin, Rohminatin Roni Dalimunthe Sahren Santoso Santoso Sapta, Andy Satria Yudha Prayogi Siagian, Yessica Sitorus Pane, Siti Fatimah Sri Ayu Rizky Sri Maharani Sudarmin Sudarmin Sudarmin Sudarmin Sutan Hirmansyah Siregar Suwati Suwati Syartika Anggraini Titin, Tri Wanti USWATUN HASANAH Uswatun Hasanah William Ramdhan Zikra Syah , Arridha Zulfira Elisa Chan