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All Journal Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Bangse, Ni Nyoman Dinda Permata Putri
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Prediksi Kelulusan Tepat Waktu Menggunakan Algoritma C 4.5 dengan Optimasi Forward Selection (Studi Kasus : Program Studi Teknik Komputer Fakultas Ilmu Komputer Universitas Brawijaya) Bangse, Ni Nyoman Dinda Permata Putri; Wijoyo, Satrio Hadi; Bachtiar, Fitra Abdurrachman
Jurnal Pengembangan Teknologi dan Ilmu Komputer Vol 10 No 13 (2026): Publikasi Khusus Tahun 2026
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

naskah ini akan diterbitkan di JITECS
Predicting On-Time Graduation Using the C 4.5 Algorithm with Forward Selection Optimization (Case Study: Computer Engineering Study Program, Faculty of Computer Science, Brawijaya University) Bangse, Ni Nyoman Dinda Permata Putri; Wijoyo, Satrio Hadi; Bachtiar, Fitra Abdurrachman
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112871

Abstract

On-time student graduation is a key indicator of academic effectiveness and higher education quality. Timely graduation reflects efficient academic management, while delays may negatively impact institutional performance and accreditation. Graduation delays are influenced by various academic and non-academic factors, making early prediction essential. This study aims to develop an on-time graduation prediction model using the C4.5 algorithm optimized with the forward selection method. The research was conducted in the Computer Engineering Study Program, Faculty of Computer Science, Universitas Brawijaya, using student data from the 2018–2021 cohorts. The dataset includes both academic and nonacademic attributes. The modeling process followed the CRISP-DM framework, and model performance was evaluated using a confusion matrix. The results show that the C4.5 model without feature selection achieved an accuracy of 63%, while the application of forward selection significantly improved accuracy to 83%. These findings indicate that feature selection plays a crucial role in enhancing prediction performance. The proposed model can support academic stakeholders in data driven decision making and in designing strategies to improve on-time graduation rates.  
Co-Authors Fitra Abdurrachman Bachtiar Satrio Hadi Wijoyo