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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Knowledge Engineering and Data Science InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika JOISIE (Journal Of Information Systems And Informatics Engineering) JISKa (Jurnal Informatika Sunan Kalijaga) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer JINAV: Journal of Information and Visualization Jurnal Pendidikan dan Teknologi Indonesia Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Masyarakat Bhinneka Journal of Training and Community Service Adpertisi Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Digital Business Jurnal Penelitian Sistem Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Journal of Enhanced Studies in Informatics and Computer Applications J-KOMA : Jurnal Ilmu Komputer dan Aplikasi International Journal of Computer Science and Information Technology Edu Komputika Journal Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of World Future Medicine, Health and Nursing
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Enhancing Adolescent Smartphone Addiction Screening Using a Weighted Radial Basis Function Neural Network Mochammad Anshori; Rosyidah Alfitri
Edu Komputika Journal Vol. 13 No. 1 (2026): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v13i1.36235

Abstract

Smartphone addiction among adolescents threatens mental health, sleep, academic performance, and social functioning, creating an urgent need for objective, scalable screening tools. This study evaluates whether a weighted RBFNN (Radial Basis Function Neural Network) with learnable per-feature weights can improve early identification of smartphone addiction in adolescents. Using a questionnaire-derived dataset of 394 participants, features were standardized, hidden-unit centers were initialized with k-means clustering, and both conventional and weighted RBFNN architectures were trained and compared under stratified ten-fold cross-validation while sweeping the number of hidden units. Models were assessed by accuracy, precision, recall, F-measure, area under the receiver operating characteristic curve, and computation time. The weighted RBFNN consistently outperformed the conventional variant and previously reported baselines, achieving a mean cross-validation accuracy 97.99% across all cluster settings, with best performance of 98.48% (at cluster 6) and an area under the curve near 0.989, with very low false positive and false negative rates and reduced computation time at larger cluster settings. Learnable feature weighting mitigated noisy predictors and improved generalization, while results underscored sensitivity to cluster-count selection and preprocessing choices. These findings indicate that a weighted RBFNN is a promising, efficient approach for automated adolescent smartphone addiction screening.