Deteksi Dini Mahasiswa Drop Out Menggunakan C5.0

Authors

  • Ulfi Saidata Aesyi Universitas Jenderal Achmad Yani Yogyakarta
  • Alfirna Rizqi Lahitani Universitas Jenderal Achmad Yani Yogyakarta
  • Taufaldisatya Wijatama Diwangkara Universitas Jenderal Achmad Yani Yogyakarta
  • Riyanto Tri Kurniawan Universitas Jenderal Achmad Yani Yogyakarta

DOI:

https://doi.org/10.14421/jiska.2021.6.2.113-119

Abstract

The decline in the number of active students also occurred at the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This greatly affects the profile of study program graduates. So it is necessary to have a system that is able to detect students who are threatened with dropping out early. In this study, the attributes chosen were the student's GPA and the percentage of attendance . This attribute is used to classify students who are predicted to drop out. The research data uses student data from the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This study uses the C5.0 algorithm to build a decision tree to assist data classification. The decision tree that was built with 304 data as training data resulted a C5.0 decision tree which had an error rate of 5%. The accuracy results obtained from the 76 test data is 93%.

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Published

2021-05-03

How to Cite

Aesyi, U. S., Lahitani, A. R., Diwangkara, T. W., & Kurniawan, R. T. (2021). Deteksi Dini Mahasiswa Drop Out Menggunakan C5.0. JISKA (Jurnal Informatika Sunan Kalijaga), 6(2), 113–119. https://doi.org/10.14421/jiska.2021.6.2.113-119

Issue

Vol. 6 No. 2 (2021): Mei 2021

Section

Articles

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Copyright (c) 2021 Ulfi Saidata Aesyi, Alfirna Rizqi Lahitani, Taufaldisatya Wijatama Diwangkara, Riyanto Tri Kurniawan

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