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All Journal Jurnal Teknik Informatika (JUTIF)
Putry Wahyu Setyaningsih
Department of Information System, Universitas Mercu Buana Yogyakarta, Indonesia
Computer Science & IT

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Permutation-Based RFECV on Imbalanced Students’ Grade and Students’ Dropout Datasets Irfan Pratama; Albert Yakobus Chandra; Putry Wahyu Setyaningsih; Husna Sarirah Husin; Odi Nurdiawan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5641

Abstract

Predicting student academic performance is a critical task in EDM. To build predictive models, it is essential to identify the most informative features from LMS data. However, existing feature selection techniques exhibit significant drawbacks. Filter Methods evaluate features independently from the learning model, ignoring interactions and context. As a result, they may select redundant features and lack adaptability to specific classifiers. To enhance classification accuracy, this research proposes a permutation-based Recursive Feature Elimination with cross-validation (RFECV) feature selection method to strengthen machine learning models' student dataset classification, thereby eliminating bias from model-based feature importance calculations using the default method. The proposed method excels on the available educational datasets (Math, Por, Student Success, and Student dataset) in terms of F1-Score. Specifically, we achieved 80.59% on math dataset and 71.81% on student success dataset, both of which are multiclass datasets.
Co-Authors Albert Yakobus Chandra Husna Sarirah Husin Irfan Pratama Odi Nurdiawan