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, 2025)
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.