Article Per Year (5 Year)
p-Index From 2021 - 2026
1.704
P-Index
This Author published in this journals
All Journal Ekonomikalia Journal of Economics Journal of Educational Management and Learning Indatu Journal of Management and Accounting Grimsa Journal of Business and Economics Studies
Ringga, Edi Saputra
Unknown Affiliation
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Educational Management and Learning

 1 
An Explainable Machine Learning Study of Behavioral and Psychological Determinants of Depression in the Academic Environment Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan; Ringga, Edi Saputra; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i1.304

Abstract

Depression is a significant and growing concern within academic environments, affecting both students and staff due to factors such as academic pressure, financial stress, and lifestyle challenges. This study explores the use of machine learning, specifically a Random Forest classifier, to predict depression risk among students using behavioral, psychological, and demographic data. A dataset of 27,788 student records was analyzed after thorough preprocessing and exploratory data analysis. The model achieved strong performance, with an accuracy of 83.52% and an AUC of 0.91, indicating reliable classification of depression status. Local Interpretable Model-agnostic Explanations (LIME) were employed to enhance interpretability, revealing key predictive features such as suicidal ideation, academic pressure, sleep duration, and dietary habits. These interpretable insights align with existing psychological research and provide actionable information for mental health professionals. The findings highlight the value of explainable AI in educational settings, offering a scalable and transparent approach to early depression detection and intervention. Future work should focus on longitudinal data integration, multimodal inputs, and real-world implementation to strengthen the model’s utility and impact.
Co-Authors Abda Abda Amalina, Faizah Asyukri, Tibyan Chenny Seftarita Fadliansah, Oka Fijay, Ade Habya Hadiyani, Rahmilia Hafizah, Iffah Hidayatullah, Ferdy Idroes, Ghalieb Mutig Idroes, Ghifari Maulana Irsan Hardi Kadri, Mirzatul Marsellindo, Rio Maulana, Aga Maulana, Ar Razy Ridha Muhammad Adam, Muhammad Nghiem, Xuan-Hoa Novita Novita Putra, Hadi Arisyah Rinaldi Idroes Silvia, Vivi Suriani Suriani Suwal, Sunil Syahyana, Ahmad Teuku Rizky Noviandy Wintara, Heri