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All Journal Jurnal Teknik Informatika dan Teknologi Informasi
Heri Setiawan
Universitas Asahan
Computer Science & IT

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Klasifikasi Resiko Depresi Berdasarkan Durasi Layar dan Pola Hidup Digital Mengguanakan Metode K-Nearest Neighbor (KNN) Heri Setiawan; Helmi Fauzi Siregar
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6366

Abstract

This study aims to classify depression risk levels based on screen time and digital lifestyle patterns using the K-Nearest Neighbor (KNN) method. The dataset used includes several important variables, such as daily screen time, frequency of social media use, and sleep duration and quality. These variables were chosen because they are considered to have a strong association with mental health, particularly depressive symptoms that often arise from excessive digital device use. A KNN model was then developed and tested to categorize individuals into three depression risk categories: low, medium, and high. The evaluation results showed that the model with a k value of 5 achieved a predictive accuracy of 85%, indicating that this method is quite effective as a data-driven classification tool. The findings of this study suggest that digital lifestyle patterns can be an early indicator in predicting depression risk, thus requiring more systematic monitoring. However, this model still needs to be combined with clinical assessment for a more comprehensive and accurate diagnosis.  
Co-Authors Helmi Fauzi Siregar