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Journal : bulletin of computer science research

Prediksi Kelulusan Mahasiswa Prodi Informatika dengan Algoritma Decision Tree (C4.5) dan Naïve Bayes Steven Gerrard; Ade Eviyanti; Hamzah Setiawan; Ika Ratna
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1035

Abstract

The primary parameter for measuring higher education quality, which also has a crucial impact on the accreditation process, is the percentage of students graduating on time. However, the reality on the ground shows that many students face obstacles in completing their studies within the ideal timeframe. Therefore, a data-driven strategy is needed to project students' chances of graduation early. This research aims to compare the performance of the Decision Tree (C4.5) and Naïve Bayes algorithms in classifying the potential for on-time graduation. The data utilized included 161 entries from the Informatics Study Program, class of 2022, at the University of Muhammadiyah Sidoarjo. The attributes analyzed were divided into academic and non-academic factors, including gender, first-semester social studies grades (IPS), GPA, PKMU (Community Service Program) graduation score and status, BQ and Ibadah scores, and accumulated SKEK points. The research process went through several phases: preprocessing, class labeling, model development, and performance evaluation through a confusion matrix and 5-fold cross-validation. The test was validated by separating the training and test data into ratios of 70:30, 80:20, and 90:10. Based on the test results, the C4.5 algorithm achieved a peak accuracy of 100% across all ratio scenarios, with an average cross-validation accuracy of 96.88%. Meanwhile, Naïve Bayes achieved a maximum accuracy of 94.13% with an average cross-validation of 93.00%. These findings indicate that the C4.5 algorithm has superior performance on this specific dataset. The output of this predictive model is expected to serve as an objective basis for institutions in establishing proactive academic policies.
Co-Authors Abidin, Husnul Ade Eviyanti Adi Putra, Lutfi Adiffanani Ramdansyah Alshaf Pebrianggara Amelia, Paramitha Angga Wibawa Saputra Angga Arief Wicaksono, Arief Arif Senja Fitrani Arif Senja Fitriani Arisandi, Ricky Renaldo Asiddiq, Afnizar Maulana Aulia Aliffiandi, Rizca Aziziyah, Ismi Anisa Azmuri Wahyu Azinar Azmuri Wahyu Azinar Cindy Taurusta Duwi Rahayu Enggi Sabrilla Assara Evi Rinata Fuad Azis Muslim Gilang Pralaya Grahita Albarika, Ayu Hari Moerti Hindarto Hindarto Hindarto Ika Ratna Ika Ratna Indra Astutik Imanda, Almyra Gitta Intan Nuraini Irwan A. Kautsar Irwan Alnarus Kautsar Jamal Hasan Jefry Fernando Kurnia Ningtiyas Luluk Asti Qomariah M Cholis Afandi M. Alfan Rosyid Moch Bagus Tri Cahyo Moch Ridwan Alwi Moch Ridwan Alwi Mochamad Alfan Rosid Mochamad Surohadi Mochammad Septa Sandy Mohamad Haris Muzadi Muhammad Agung Laksono Muhammad Fikri Muhammad Mursidil Arif Muhammad Saddam Heykal Bustomy Muhammad, Fajar Muhammad, Khithoh Sabda Nanda Fitriana Novia Ariyanti Nur Maslikhatun Nisak Nuril Lutvi Azizah Paramitha Amelia Kusumawardani Pratiwi, Rosa Machmuda Qur'ani, Meisyilia Difanada Rachmat Firdaus Ratih Sri Yunarti Rayhanantha Akbar Putra Prasetyo Ribangun Bamban Jakaria Rina Safitri Riswanto Rizky Budi Aprianto Rizky Rahmahdian Sandy Rohman Dijaya sandy, Mochamad septa Saputra Budianto Putra Senja Fitrani, Arief Sinta Nuriyah, Rizky SITI CHOLIFAH Siti Cholifah Siti Cholifah Steven Gerrard Sumarno . Sumarno Sumarno Suprianto Suprianto1, Suprianto Triwahono, Handi Uce Indahyanti Usqi Salsabila, Firdausi Vidya Wati Dwi Ramadhani Wildan Arif Hidayatulloh Wirabumi Putra, Cakra Wiwik Sumarmi Yunianita Rahmawati Yunianita Rahmawati Zulham Efendi, Muhammad