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Prediction of Informatics Program Students' Graduation Using Decision Tree (C4.5) and Naïve Bayes Algorithms

Prediksi Kelulusan Mahasiswa Prodi Informatika dengan Algoritma Decision Tree (C4.5) dan Naïve Bayes

##article.authors##

  • Steven Gerrard Universitas Muhammadiyah Sidoarjo
  • Ade Evianti
  • Hamzah Setiawan
  • Ika Ratna

DOI:

https://doi.org/10.21070/ups.10705

Keywords:

Data Mining, Naïve Bayes, C4.5, Akademik, Prediksi

Abstract

On-time graduation is a primary metric for measuring higher education quality and accreditation. However, many students face obstacles completing their studies within the ideal timeframe. Therefore, this research compares the Decision Tree (C4.5) and Naïve Bayes algorithms to classify the potential for on-time graduation early. The dataset utilizes 161 student entries from the 2022 Informatics Study Program at the University of Muhammadiyah Sidoarjo. Analyzed attributes encompass academic and non-academic factors, including gender, social studies grades, GPA, PKMU, BQ, Ibadah, and SKEK points. The research process involved preprocessing, class labeling, modeling, and evaluation through a confusion matrix and 5-fold cross-validation across 70:30, 80:20, and 90:10 data splits. Results demonstrate that C4.5 achieved 100% peak accuracy and a 96.88% cross-validation average. Meanwhile, Naïve Bayes reached 94.13% maximum accuracy and a 93.00% average. Ultimately, C4.5 showed superior performance on this dataset , offering an objective tool for institutions to establish proactive academic policies

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2026-04-20

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