DATA MINING MENGGUNAKAN ALGORITMA C4.5 UNTUK MEMPREDIKSI KEPUASAN MAHASISWA TERHADAP KINERJA DOSEN DI KOTA BATAM

  • Yulia Yulia Universitas Putera Batam
  • Anggia Dasa Putri

Abstract

The University is an institution of higher education and research that provides academic degrees in various fields. One of the vision and mission of Higher Education in the city of Batam is to become a leading tertiary institution and to carry out high quality education with the aim of producing quality graduates and competent human resources in their fields. One of the influencing factors is the lecturer performance. This study aims to determine the level of student satisfaction that is influenced by lecturer performance in lectures with data mining techniques using the C4.5 algorithm where the variables used include reliability, responsiveness, appearance, empathy and assurance. The research objective is to predict the level of student satisfaction with the performance of University Lecturers in the city of Batam in the teaching and learning process. The benefit of this research is to find out the performance of a College Lecturer in order to produce quality teaching staff and quality education. Data Mining with C4.5 Algorithm, the process of classifying data and looking for patterns so as to produce interesting information in data in decision making. Based on the results of research conducted, the decision tree with manual calculation of the C4.5 algorithm is the same as the decision tree produced by Weka software with the resulting accuracy level of 94.12%. Then it can be concluded the prediction of student satisfaction on the lecturer performance is satisfied.

Published
2019-09-27
How to Cite
YULIA, Yulia; PUTRI, Anggia Dasa. DATA MINING MENGGUNAKAN ALGORITMA C4.5 UNTUK MEMPREDIKSI KEPUASAN MAHASISWA TERHADAP KINERJA DOSEN DI KOTA BATAM. Computer Based Information System Journal, [S.l.], v. 7, n. 2, p. 56-66, sep. 2019. ISSN 2621-5292. Available at: <http://ejournal.upbatam.ac.id/index.php/cbis/article/view/1373>. Date accessed: 08 apr. 2020. doi: https://doi.org/10.33884/cbis.v7i2.1373.