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All Journal Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Latius Hermawan
Universitas Sriwijaya
Education Engineering Industrial & Manufacturing Engineering

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EARLY DETECTION OF ACADEMIC DEPRESSION USING SMARTPHONE-BASED MACHINE LEARNING MODELS Edi Surya Negara; Latius Hermawan; Hastari Mayrita; Desy Arisandy; Mohamad Farozi; Rahmat Ramadan; Sunda Ariana; Ria Andryani
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 3 (2025): Volume 9, Nomor 3, September 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i3.46375

Abstract

Mental health in developing countries is a common and complex problem. The problem continues to increase and is closely related to low self-confidence, negative interpersonal relationships, and academic depression. This can affect students' ability to complete academic assignments on a university scale. An AI-based early detection application can potentially improve mental health services related to treatment access. This system can help identify users who may be depressed based on the language used, especially for those who are reluctant to seek professional solutions due to the negative stigma of mental health. This study uses a qualitative descriptive method involving observation, in-depth analysis of group conversations, and early detection of academic depression by identifying conversation patterns between students and counselors as the basis for developing a smartphone-based application. This study produced a dataset of 395 depression-level data entries used as training data to develop a machine-learning model. A prototype of an academic depression detection application has been developed as a mobile-based application.
Co-Authors Andryani, Ria Desy Arisandy Edi Surya Negara Hastari Mayrita Mohamad Farozi Rahmat Ramadan Sunda Ariana, Sunda