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Jurnal Ilmiah Intech : Information Technology Journal of UMUS
Published by Universitas Muhadi Setiabudi
ISSN : -     EISSN : 26854902     DOI : 10.46772/intech
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah Intech : Information Technology Journal of UMUS terbit 2 (dua) kali dalam satu tahun yaitu Mei dan November yang berisi hasil pemikiran dan penelitian pada bidang Ilmu Komputer dan Informatika, dnegan fokus dan ruang lingkup: 1. ilmu komputer: Artificial Intelligence, Machine Learning, Data Mining, Expert System, Decission Support System 2. Informatika : Web Programming, Mobile Computing, Computer Network, Pembuatan Sistem Informasi, Database System, Security System.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 111 Documents
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Predicting Depression Risk Levels in College Students Using the K-Nearest Neighbor Algorithm Attagantari Ardelia Retna, Atta; Arief Hidayat, Arief Hidayat; Akhmad Pandhu Wijaya, Akhmad Pandhu Wijaya
Jurnal Teknik Informatika UMUS Vol 7 No 2 (2025): November
Publisher : Universitas Muhadi Setiabudi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract College students often experience academic, social, and personal stress that can lead to mental health issues such as depression. If left untreated, this condition can disrupt academic achievement and social relationships, and even trigger extreme behavior. This study aims to design and develop a system to predict the risk of depression in college students using the K-Nearest Neighbor (KNN) classification algorithm. Data were collected from 300 students through a Likert-scale questionnaire. The system was developed using Python for pre-processing. The analysis process included data selection, cleaning, transformation, classification with KNN, and performance evaluation using a confusion matrix. Testing of 60 test data sets yielded 85% accuracy, 90% precision, and 83% recall. This system has the potential to be used as an early detection tool for students and educational institutions. Keywords : Depression, Students, K-Nearest Neighbor, Classification, Data Mining

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