ANALISIS KOMPARASI ALGORITMA C5.0 DAN NAIVE BAYES PENENTUAN PENERIMA BEASISWA UNIVERSITAS PRIMA INDONESIA

  • Carolus Laberto Fantasy Universitas Prima Indonesia
  • Felix Luther Mateus Simanjuntak Universitas Prima Indonesia
  • Raja Levi Aldi Purba Universitas Prima Indonesia
  • Oloan Sihombing

Abstract

In developing quality human resources, Prima Indonesia University offers a scholarship program to help with educational costs for outstanding students. This research aims to help solve the problem of scholarship recipient selection which requires in-depth analysis using data mining technology. In this research, the use of the C5.0 algorithm and Naive Bayes algorithms was compared in determining scholarship recipients at Prima Indonesia University. The research method involves research locations at Prima Indonesia University using scholarship student data for 2019-2022 as research objects. The research instrument includes the use of the Python programming language with Google Colab as an editor, the Windows 10 operating system, and hardware with certain specifications. Data collection involves observation, literature study, data cleaning, data mining, and exploratory data analysis. The results of research using and comparing the C5.0 and Naive Bayes algorithms show an accuracy of 98.62% and 91.37% respectively. Evaluation involves precision, recall, F1, and confusion matrix values. In conclusion, the C5.0 algorithm is more accurate in determining scholarship eligibility than Naive Bayes, with accuracy increasing by around 8%. This research contributes to the development of data mining and predictive analysis in the context of determining scholarship recipients in higher education institutions.

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Published
2023-12-27
How to Cite
FANTASY, Carolus Laberto et al. ANALISIS KOMPARASI ALGORITMA C5.0 DAN NAIVE BAYES PENENTUAN PENERIMA BEASISWA UNIVERSITAS PRIMA INDONESIA. Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 6, n. 2, p. 508-517, dec. 2023. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/926>. Date accessed: 05 mar. 2024. doi: https://doi.org/10.37600/tekinkom.v6i2.926.
Section
Articles