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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Lasia Agustina
Universitas Negeri Jakarta

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An artificial neural network-based decision support model for early prediction of mathematics learning challenges using the CRISP DM framework Harry Dhika; Surajiyo Surajiyo; Lasia Agustina; Abdul Muchlis
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp618-627

Abstract

Identifying mathematics learning difficulties remains a challenge for educators due to the subjectivity and inefficiency of conventional methods in capturing psychological factors. To address this limitation, this study proposes an artificial neural network (ANN)-based decision support model developed within the cross-industry standard process for data mining (CRISP-DM) framework. The model integrates 16 academic indicators (quizzes, exams, remedial frequency, online activity) and psychological factors (motivation, anxiety, self-confidence, interest) from 163 student records at SMA Muhammadiyah 16 Jakarta. Synthetic minority over-sampling technique (SMOTE) and focal loss are applied to handle class imbalance and improve reliability. The proposed model achieves 98% accuracy and a 0.97 F1-score in classifying students into three difficulty levels: Easy, moderate, and difficult. These findings demonstrate the model’s effectiveness in capturing complex relationships between cognitive and affective features. Unlike prior studies that rely solely on academic performance, this work contributes a robust, comprehensive data-driven framework that enhances multiclass classification for early educational intervention within the Indonesian context.
Co-Authors Abdul Muchlis Harry Dhika, Harry Surajiyo Surajiyo