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All Journal JURNAL SISTEM INFORMASI BISNIS MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi dan Manajemen Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi ABIDUMASY : Jurnal Pengabdian kepada Masyarakat JURNAL ILMIAH PENELITIAN MAHASISWA Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi Journal of Engineering, Electrical and Informatics
Tanhella Zein Vitadiar
Sistem Informasi, Fakultas Teknologi Informasi, Universitas Hasyim Asy’ari Tebuireng Jombang
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Social Sciences Other

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Decision Support System for Identifying Student Learning Styles in Elementary School using Naïve Bayes Algorithm khakim, ikhsanul; Mujianto, Ahmad Heru; Vitadiar, Tanhella Zein; Mashuri, Chamdan
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.39725

Abstract

Identifying student learning styles is essential for teachers to design effective and adaptive teaching strategies. At SDN Rejoagung 3, this process is currently conducted manually through observation and interviews, which are prone to subjective bias. This research develops a web-based decision support system to classify student learning styles—Visual, Auditory, and Kinesthetic—using the Naïve Bayes algorithm. The system was built using data collected via questionnaires from students in grades 1 to 6. Testing was conducted using a confusion matrix to evaluate the model's performance. The results show that the Naïve Bayes algorithm successfully classified learning styles with an accuracy of 94.12%. This system provides a more objective and systematic tool for teachers to identify students' preferences, enabling more personalized instructional delivery in an elementary school context
Co-Authors Achmad Imam Agung Akhmad As Sakhaf Aldi Kurniawan Alfi Azqia Uzzulfa Haq Amruli, Danang Rifki Anita Andriani Aries Dwi Indriyanti, Aries Dwi BAMBANG SUJATMIKO Enrico Yoga Kusuma Ginanjar Setyo Permadi Hadi Sucipto Hadi Sucipto, Hadi Hery Kristianto I Gusti Lanang Eka Prismana I Kadek Dwi Nuryana Iftitaahul Mufarrihah Imron Rosadi khakim, ikhsanul Kistofer, Terdy Kristianto, Hery Kusmiatun M. Aufa Alfis Syihab Mahrus Ali Mashuri, Chamdan Muhammad Amirudin Muhammad Fatkhur Rizal Muhmmad Fatkhur Rizal Mujianto, Ahmad Heru Pawindyanto, Aditya Danang Resty Alfianda Zellyanawati Slamet Wirawan Terdy Kistofer UMAR FAUZI