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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Larasati, Indah Paksi
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Computer Science & IT Engineering

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Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction Haryono Setiadi; Larasati, Indah Paksi; Esti Suryani; Wardani, Dewi Wisnu; Wardani, Hasan Dwi Cahyono; Ardhi Wijayanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5911

Abstract

Predicting student dropout is essential for universities dealing with high attrition rates. This study compares two feature selection (FS) methods—correlation-based feature selection (CFS) and symmetrical uncertainty (SU)—in educational data mining for dropout prediction. We evaluate these methods using three classification algorithms: decision tree (DT), support vector machine (SVM), and naive Bayes (NB). Results show that SU outperforms CFS overall, with SVM achieving the highest accuracy (98.16%) when combined with SU Moreover, this study identifies total credits in the fourth semester, cumulative GPA, gender, and student domicile as key predictors of student dropout. This study shows how using feature selection methods can improve the accuracy of predicting student dropout, helping educational institutions retain students better.
Co-Authors Ardhi Wijayanto Dewi Wisnu Wardani Esti Suryani Haryono Setiadi Wardani, Hasan Dwi Cahyono