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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Al-Hikmah: Jurnal Agama dan Ilmu Pengetahuan Jurnal Psikologi Jambi Nathiqiyyah : Jurnal Psikologi Islam Jurnal Diferensial Al-'Adad: Jurnal Tadris Matematika Global Perspectives in Education Journal Persepsi: Jurnal Riset Mahasiswa Psikologi
Ulfa, Fara
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Enhancing Student Performance Classification Through Dimensionality Reduction and Feature Selection in Machine Learning Mustakim, Mustakim; Sari, Windy Junita; Ulfa, Fara
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37783

Abstract

Education plays an important role in shaping the intellectual and character of the nation's next generation. However, poor student academic performance is a major challenge, especially regarding student retention and dropout risk. This study aims to evaluate the performance of machine learning algorithms, namely K-Nearest Neighbor (K-NN), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGB), and analyze the effect of dimensionality reduction using Principal Component Analysis (PCA) and feature selection with Recursive Feature Elimination (RFE) on student performance prediction accuracy. The research dataset consists of 395 student samples with demographic, social, and academic attributes. The results show that XGB has the best performance with 98.32% accuracy and can predict all classes with perfect 100% accuracy. LightGBM and K-NN achieved 94.87% and 93.88% accuracy, respectively. The best attributes affecting student performance were found in the “Highly Prioritized” category, including study time, family support, family, and health. Although PCA slightly degraded the model performance, feature selection with RFE significantly improved accuracy. This study concludes that proper algorithm selection and focus on relevant attributes can improve prediction accuracy and efficiency, making an important contribution to the development of more effective education prediction systems.
Co-Authors Agustin, Liliza Ajrah, Nabila Atika Clara Dwi Oktaviani Fazril, Raisya Azzahra Jannah, Putri Miftahul Jayawarsa, A.A. Ketut Mustakim Mustakim Qudri, Tarania Raudatussalamah Raudatussalamah Raudatussalamah Salmiyati Sarbaini Sarbaini Sari, Windy Junita Susanti , Reni Syifa, Rana Winda, Yuliani Yuli Widiningsih, Yuli