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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Rahim, Arham
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Mathematics Physics

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Supervised Machine Learning for Prediction of Minimum Completeness Criteria (KKM) Scores for Elementary School Students Mustakim; Rahim, Arham
Jurnal Penelitian Pendidikan IPA Vol 10 No 11 (2024): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i11.9258

Abstract

This study aims to predict potential declines in students' Minimum Completeness Criteria (KKM) in higher grades (4th, 5th, and 6th) by analyzing their cognitive, affective, and psychomotor scores from lower grades (1st, 2nd, and 3rd). Using a quantitative research method, various machine learning algorithms were applied, including Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Neural Networks. The dataset comprised students' scores across cognitive, affective, and psychomotor domains from the lower grades. After training and comparing the models, the Neural Network algorithm demonstrated the best performance, achieving 89% accuracy and 100% recall. These results indicate that the model can help teachers identify students at risk of struggling with KKM standards in higher grades, enabling early interventions. The study concludes that Neural Networks offer a promising tool for early detection of academic challenges in elementary education.
Peningkatan Akurasi Model Untuk Prediksi KKM Siswa Sekolah Dasar Menggunakan Supervised Machine Learning dengan Integrasi Faktor Internal dan Eksternal Rahim, Arham; Mustakim, Mustakim
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34577

Abstract

The Minimum Mastery Criteria (KKM) is a standard used to assess students’ competency achievement in elementary schools in Indonesia and serves as an important indicator of learning success. However, many students still have difficulties meeting this standard, thus requiring a data-driven early detection strategy to support timely intervention. This study aims to develop a prediction model for students’ KKM achievement based on internal and external factors using a supervised machine learning approach. Internal data include report card scores and attendance, while external data are obtained from student responses and parental information covering environmental, economic, motivational, and family support aspects. Four machine learning algorithms were evaluated, namely Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Neural Network, using a confusion matrix. Experiments were conducted under four data preprocessing scenarios: reverse scoring, feature selection, normalization, and variable grouping. The best result was obtained in Scenario S3, which combines normalization and feature selection, using the SVM algorithm with 100% accuracy. However, to avoid potential overfitting, a more stable algorithm is recommended, namely Naïve Bayes, which achieved 93% accuracy. These results indicate that the application of machine learning with appropriate preprocessing is effective for identifying students at risk of not achieving the KKM.
Co-Authors Mustakim Mustakim Mustakim