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All Journal JAIS (Journal of Applied Intelligent System) Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics MEANS (Media Informasi Analisa dan Sistem) Didaktika: Jurnal Kependidikan JURNAL ILMIAH GLOBAL EDUCATION Jurnal Algoritma Jurnal Cakrawala Informasi (JCI) Jurnal Masyarakat Madani Indonesia "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Journal of Future Artificial Intelligence and Technologies Jurnal Informatika dan Kesehatan Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat INOVTEK Polbeng - Seri Informatika
Iwan Setiawan Wibisono
Universitas Ngudi Waluyo
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Comparison of K-Nearest Neighbor, Naïve Bayes, and C4.5 Algorithms for Predicting Academic Stress Risk in Students Based on Psychological Survey Data Widya Pertiwi, Nur Annisa; Wibisono, Iwan Setiawan
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11932

Abstract

Academic stress is a psychological problem experienced by many students and can have an impact on learning achievement, mental health, and quality of life. This study aims to compare the performance of the K-Nearest Neighbor (KNN), Naïve Bayes, and C4.5 (Decision Tree) algorithms in predicting the level of academic stress risk in students based on psychological survey data. Data were obtained from 700 active students at Ngudi Waluyo University through a questionnaire covering physiological, psychological, and behavioral aspects, with a total of 15 indicators using a Likert scale. The data then underwent pre-processing, labeling, standardization, and division into training and test data with a ratio of 80:20. The evaluation was conducted using the Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and AUC-ROC metrics. The results showed that the Naïve Bayes algorithm performed best with an accuracy of 93.26%, precision of 93.35%, recall of 92.26%, and F1-score of 92.80%. The KNN algorithm was in second place with an accuracy of 91.43%, while the C4.5 algorithm had the lowest performance with an accuracy of 80.60%. Based on these results, Naïve Bayes is recommended as the most optimal algorithm for predicting academic stress risk in students using psychological survey data. This study is expected to assist educational institutions in identifying students at risk of stress early on and supporting the development of more effective prevention strategies.
Co-Authors Abdul Rohman Achmad Nuruddin Safriandono Akhmad Dahlan Alfiano Aldo Pamungkas amalia fatima zahra Aqmal Putra R.R Arga Aditia Purnama Aya Sofia Dinafa C, Annisa De Rosal Ignatius Moses Setiadi Efaforito Gulo Farah Zakiyah Rahmanti Fitri Anisa Rahman Heni Candra Kirana Heni Setyowati Hesti Yunitiara Rizqi Himmah Taulany inneke fitri ade muliya Kartika Yuni Purwanti Kurniati Pratami Lisa Virdinarti Putra Mamluatul Husnia Marsiska Ariesta Putri Nurohmah, Siti Ojugo, Arnold Adimabua Sanjaya, Ucta Pradema Sekar Febri Medianza Sri Mujiyono Suamanda Ika Novichasari Tatik Wulandari Widya Pertiwi, Nur Annisa Zilni, An Zulmi Roestika Rini