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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Diklat Review : Jurnal manajemen pendidikan dan pelatihan Journal of Informatics Management and Information Technology Mejuajua SAINSTEK
Fadhilla, Mutia
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health Other

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Journal : Journal of Informatics Management and Information Technology

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Analisis Performa Algoritma Machine Learning Untuk Identifikasi Depresi Pada Mahasiswa Fadhilla, Mutia; Wandri, Rizky; Hanafiah, Anggi; Rachmat Setiawan, Panji; Arta, Yudhi; Daulay, Suandi
Journal of Informatics Management and Information Technology Vol. 5 No. 1 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i1.473

Abstract

Mental health, especially depression, is a major issue among college students due to academic, social, and social media pressures. Depression detection faces challenges such as stigma, low literacy, and ineffective conventional methods. Machine learning technology offers solutions with algorithms such as Naive Bayes, SVM, and Random Forest to improve detection accuracy, support early intervention, and improve the student mental health system. Mental health, especially depression, is a major issue among college students due to academic, social, and social media pressures. Depression detection faces challenges such as stigma, low literacy, and ineffective conventional methods. Machine learning technology offers solutions with algorithms such as Naive Bayes, K-Nearest Neighbor, Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine to improve detection accuracy, support early intervention, and improve the student mental health system. Based on the results of the performance analysis of the machine learning algorithm, the most effective model in predicting depression status in students is Logistic Regression which has an accuracy rate of 95.62%. As a strategic step, machine learning technology can be integrated for early diagnosis of depression in students. This system is expected to be more effective and efficient, improve diagnostic accuracy, and open up opportunities for new approaches to responsive, data-driven mental health.
Co-Authors ANDRI KURNIAWAN Baskara, Agus Daulay, Suandi Des Suryani Fadhilah, M Rizki Fiqri Qurniawan, Dwi Hanafiah, Anggi Masnur Masnur Muhammad Rasyid Abdillah Nur, Nuriman M Oktasia Nasution, Hafiza Rachmat Setiawan, Panji Rizky Wandri Rizqa Anita Sapitri Sapitri, Sapitri Setiawan, Panji Rachmat Syafitri, Nesi Syefriani Yudhi Arta, Yudhi