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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF SCIENCE AND SOCIAL RESEARCH International Journal of Advances in Data and Information Systems INFOKUM Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Jurnal Minfo Polgan (JMP) Journal of Science Technology (JoSTec) Jurnal Pengabdian Masyarakat Nusantara Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Pengabdian kepada Masyarakat Jurnal Pustaka Dianmas Journal of Technology and Computer (JOTECHCOM) LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics JIPITI: Jurnal Pengabdian kepada Masyarakat International Journal of Applied Mathematics and Computing. International Journal of Educational Insights and Innovations (IJEDINS) Outline Journal of Community Development
M. Rhifky Wayahdi
Universitas Battuta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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Predicting Smartphone Addiction Levels with K-Nearest Neighbors Using User Behavior Patterns Wayahdi, M. Rhifky; Ruziq, Fahmi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4905

Abstract

Smartphones have become an integral part of everyday life, but their ever-increasing popularity has raised growing global concerns about excessive use (nomophobia), which impacts quality of life, mental health, and academic performance. Existing research often relies on subjective questionnaires, limiting scalability and objectivity. This study addresses this gap by developing a machine learning model to predict smartphone addiction levels through an objective analysis of user behavior patterns. This research evaluates the effectiveness of the K-Nearest Neighbor (KNN) algorithm, identifies the most influential behavioral features, and assesses the model's classification performance. Using a dataset of 3,300 user behavior entries with 11 features, a waterfall-based framework was employed for data preprocessing, model design, and evaluation. The KNN model achieved 95% accuracy in classifying addiction levels. Permutation Feature Importance analysis confirmed ‘App Usage Time’ and ‘Battery Drain’ as the two most influential predictive features. This study demonstrates that KNN is a powerful and viable method for objectively classifying smartphone addiction. The findings provide a strong foundation for developing scalable, AI-driven early detection and intervention systems, offering significant contributions to the fields of computer science and digital well-being.
Web-Based Diabetes Risk Prediction System Using K-NN on Kaggle Early Stage Diabetes Dataset Ruziq, Fahmi; Wayahdi, M. Rhifky
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5277

Abstract

Diabetes mellitus affects approximately 537 million adults globally, and its rising prevalence poses serious health and economic burdens. Early detection is crucial to reduce risks of complications and improve patient outcomes. This study aims to design and implement a web-based diabetes risk prediction system using the K-Nearest Neighbors (K-NN) algorithm to support early detection based on symptoms. The system utilizes the Kaggle Early Stage Diabetes Risk Prediction Dataset containing 520 records with 17 symptom attributes and one class label. Data preprocessing includes converting categorical data into numerical values, discretizing age into predefined ranges, and applying min-max scaling to normalize feature values. K-NN classification was conducted with K values of 1, 3, and 5, using the PHP Machine Learning (PHP-ML) library and MySQL database integration. The system achieved its highest accuracy of 93.46% at K = 1. Manual testing confirmed that the system processes symptom inputs correctly and provides predictions consistent with training data. This web-based tool offers an accessible platform for early diabetes risk screening, supporting self-assessment and triage. It demonstrates that PHP-ML can effectively implement machine learning in a web environment and can be further enhanced through parameter optimization and integration with larger, more diverse datasets to strengthen generalization.
Co-Authors Anzani, Nurul Baginda Harahap Davita, Davita Dewi Wahyuni Dewi Wahyuni Dinur Syahputra Dinur Syahputra Dinur Syahputra Ellanda Purwawijaya Ginting, Subhan Hafiz Nanda Guntur, Surya Hasibuan, Eka Hayana Hutasoit, Rutmian Mariama Manza, Yuke Mughnyanti, Mayang Nanda Ginting, Subhan Hafiz Nur Alisya, Siti Nurhajijah, Nurhajijah Ramadhan, Wisnu Ramadhani, Syafitri Rutmian Mariama Rutmian Mariama Hutasoit ruziq, fahmi Sheila Try Thania Sridewi, Nurmala Subhan Hafiz Nanda Ginting Subhan Hafiz Nanda Ginting Subhan Hafiz Nanda Ginting Surya Darma Surya Guntur Syed Hassan Ali Thania, Sheila Try Zaki, Mohammed