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All Journal KACANEGARA Jurnal Pengabdian pada Masyarakat KOMPUTIKA - Jurnal Sistem Komputer Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Abdimas Ilmiah Citra Bakti (JAICB) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Rambideun : Jurnal Pengabdian Kepada Masyarakat Brilliance: Research of Artificial Intelligence Buletin Sistem Informasi dan Teknologi Islam Digital Transformation Technology (Digitech) Jurnal Kolaborasi Sains dan Ilmu Terapan CSRID DIPANEGARA KOMPUTER TEKNOLOGI INFORMATIKA (DIPAKOMTI) DIPANEGARA KOMPUTER SISTEM INFORMASI (DIPAKOMSI) Journal of Information Technology and its Utilization
Madyana Patasik
Universitas Dipa Makassar
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Learning Difficulty Levels Prediction of Elementary School Student Mathematics Using Machine Learning Model Rismayani Rismayani; Novita Sambo Layuk; Madyana Patasik; Andi Hutami Endang
Journal of Information Technology and Its Utilization Vol 8 No 1 (2025): June 2025
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.8.1.5906

Abstract

Difficulty learning mathematics in elementary school students is a significant problem and requires serious attention. This study aims to predict the difficulty level in elementary school students learning mathematics using a machine learning model, namely KNN. Exam scores, assignments, quizzes, and characteristics of students' difficulty level in learning mathematics were used as data in this study. A study used the KNN model to divide students into three categories of difficulty in learning mathematics: easy, moderate, and challenging. The results showed that the KNN model can accurately predict student’s difficulty levels in mathematics. Thus, applying this model can help teachers provide appropriate and effective interventions to students experiencing difficulties. Using machine learning technology, especially the KNN model, we found an accuracy of 95%. In addition, we can still accurately predict the difficulty level of elementary school students' mathematics learning. This study uses anonymous student data, the distribution of assignments, quizzes, and exam score ranges, and characteristics of mathematics learning difficulty levels. There are three prediction classes: high, medium, and low.
Co-Authors Abdul Ibrahim Ade Sri Devy Ahmad Ahmad Ahyuna Ahyuna Ahyuna Ahyuna, Ahyuna Aini Nurul Akbar Bahtiar Akhriana, Asmah Andi Hutami Endang, Andi Hutami Andi Saenong Andrew Ridow Johanis M Arham Arifin Arifin, Suci Ramadhani Arwansyah Asmah Akhriana Chayant Issan Christiani Olivia Kondorura Cucut Susanto, Cucut Djafar, Imran Djafar, Imran Faizal Faizal Hanapi, Sitti Harlina Harlina, Sitti Hasyrif SY Heriadi Heriadi Herlinda Herlinda Herlinda Herlinda, Herlinda Husain Husain Inayah Rizkia Kaimudin Iskandar Iskandar Khaddafi, Muh. Layuk, Novita Sambo M, Muhardi M. Ibrahim M. Ibrahim, M. Magfirah Magfirah Magfirah, Magfirah Martina Pineng Muh. Khaddafi MUHAMMAD RIZAL Muhammad Rusdi Rahman Muhardi Muhardi Nasriani Nasriani Nasriani Nasriani Novita Sambo Layuk Nurani Nurani Nurdiansah Nurlina Nurlina Nurlina Nurlina Nurul Aini Piu, Sri Wahyuningsi R, Thabrani Rahmat Rahmat Rauf, Abdul Rhonaldo Pararuk Rismayani Rismayani Sadly Samsuddin Sadly Syamsuddin Salman Salman Samsu Alam Samsuddin, Sadly Santi Santi Satrina Satrina Sitti Aisa Sitti Aisa, Sitti Sitti Asnany Sri Wahyuni Suci Ramadhani Arifin Sunardi Sunardi Suryani Suryani Suryani Suryani Syahputri, Andhiny Nurakzhany Usman Usman Usman Usman Usman Wilem Musu Zeannyfer, Stiffany Lourens