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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer)
Putri, Indah Arsita
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Computer Science & IT

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PERBANDINGAN ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK KLASIFIKASI PENERIMA BEASISWA BANK INDONESIA SUMATERA SELATAN Putri, Indah Arsita; Indah, Dwi Rosa; Firdaus, Mgs Afriyan; Sari, Purwita
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1733

Abstract

This study aims to compare the performance of the Decision Tree (C4.5) and Naïve Bayes algorithms in classifying Bank Indonesia scholarship recipients based on data from the 2023-2024 academic year. The CRISP-DM methodology was applied, with model evaluation conducted using 10-fold cross-validation and metrics such as accuracy, precision, recall, and F-measure. The results indicate that the Decision Tree (C4.5) algorithm outperformed Naïve Bayes, achieving 82.70% accuracy, 98% precision, 84.07% recall, and a 90.5% F-measure. In comparison, Naïve Bayes obtained 82.21% accuracy, 97.43% precision, 83.99% recall, and a 90.2% F-measure. Although the Decision Tree (C4.5) requires slightly longer analysis time, it proved to be more effective for this classification task. This study concludes that Decision Tree (C4.5) is the most suitable method for supporting scholarship selection processes, providing new insights into applying data mining technology to improve selection efficiency and accuracy.
Co-Authors Dwi Rosa Indah Mgs Afriyan Firdaus Purwita Sari, Purwita