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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-08-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5911 |
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| author | Haryono Setiadi Indah Paksi Larasati Esti Suryani Dewi Wisnu Wardani Hasan Dwi Cahyono Wardani Ardhi Wijayanto |
| author_facet | Haryono Setiadi Indah Paksi Larasati Esti Suryani Dewi Wisnu Wardani Hasan Dwi Cahyono Wardani Ardhi Wijayanto |
| author_sort | Haryono Setiadi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3e53b2dce14e4c8d95168745de70b066 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-3e53b2dce14e4c8d95168745de70b0662025-01-13T03:33:02ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-08-018454255410.29207/resti.v8i4.59115911Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout PredictionHaryono Setiadi0Indah Paksi Larasati1Esti Suryani2Dewi Wisnu Wardani3Hasan Dwi Cahyono Wardani4Ardhi Wijayanto5Universitas Sebelas MaretUniversitas Sebelas MaretUniversitas Sebelas MaretUniversitas Sebelas MaretUniversitas Sebelas MaretUniversitas Sebelas MaretPredicting 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.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5911educational data miningperformance evaluationcorrelation-based feature selectionsymmetrical uncertainty |
| spellingShingle | Haryono Setiadi Indah Paksi Larasati Esti Suryani Dewi Wisnu Wardani Hasan Dwi Cahyono Wardani Ardhi Wijayanto Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) educational data mining performance evaluation correlation-based feature selection symmetrical uncertainty |
| title | Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction |
| title_full | Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction |
| title_fullStr | Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction |
| title_full_unstemmed | Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction |
| title_short | Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction |
| title_sort | comparing correlation based feature selection and symmetrical uncertainty for student dropout prediction |
| topic | educational data mining performance evaluation correlation-based feature selection symmetrical uncertainty |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5911 |
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