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All Journal CESS (Journal of Computer Engineering, System and Science) JURNAL TEKNOLOGI DAN OPEN SOURCE Journal of Computer Networks, Architecture and High Performance Computing Jurnal Kolaboratif Sains Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Journal Of Informatics And Busisnes
Mhd. Furqan
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Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Other

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Comparison of Machine Learning Classification Algorithm Performance for Depressive Symptom Recognition in College Students Arinda Aulia; Falah Affandi; Puan Syaharani Sitorus; Chairil Umri; Ferizal Fadli Tanjung; Mhd. Furqan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1998

Abstract

College students are vulnerable to depressive symptoms due to academic, social, and personal pressures, which can impact mental health and academic achievement. Early detection is necessary to prevent this condition from developing into a more serious condition, but conventional methods often lack objectivity. With the development of artificial intelligence, machine learning classification algorithms offer a more accurate approach to recognizing patterns of depressive symptoms. This study compared the performance of several classification algorithms, namely Random Forest, K-Nearest Neighbor, Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machine, using a dataset of depressive symptoms in college students. Evaluation was carried out based on accuracy, precision, recall, and F1-score. The results showed that Logistic Regression achieved the best performance with an accuracy of 95.62%. This suggests that selecting the right algorithm can improve the effectiveness of early depression detection systems in college students and support data-driven mental health efforts.
Co-Authors Adli Rahman Harun Harahap Aina Nur Nabila Alvi Nur Ilmi Br Ginting Alwi Syahputra Arinda Aulia Chairil Umri Dimas Aqila Aptanta Falah Affandi Fauhan Alfarizi Saragih Ferizal Fadli Tanjung Hasibuan, Suci Adina Ramadani Lubis , Aidil Halim M. Rifqi Arrafi Manihuruk , Selly Novia Meutia Nanda Muhammad Farhan Nazifa Edilia, Fazila Prananda, Dimas Raka Puan Syaharani Sitorus Ritonga, Abdul Rois Rusma Riansyah Siti Saniah Tiara Amanda Lestari Windi Imelda Putri