Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction

Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction

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 u...

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Bibliographic Details
Main Authors: Haryono Setiadi, Indah Paksi Larasati, Esti Suryani, Dewi Wisnu Wardani, Hasan Dwi Cahyono Wardani, Ardhi Wijayanto
Format: Article
Language:English
Published: Ikatan Ahli Informatika Indonesia 2024-08-01
Series:Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Subjects:
educational data mining
performance evaluation
correlation-based feature selection
symmetrical uncertainty
Online Access:https://jurnal.iaii.or.id/index.php/RESTI/article/view/5911
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https://jurnal.iaii.or.id/index.php/RESTI/article/view/5911

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