PERBANDINGAN ALGORITMA C5.0 DAN K-MEANS CLUSTERING UNTUK MEMPREDIKSI KEPUASAN MAHASISWA TERHADAP KINERJA DOSEN UNIVERSITAS PRIMA INDONESIA - Jefri Syah Putra Laoli; Sadarman Zebua; Novanius Lahagu; Delima Sitanggang; Evta Indra (Jurnal Tekinkom (Teknik Informasi dan Komputer), 2023)
Data mining is an attempt to dig up valuable and useful information on very large databases. Data mining is an operation that uses certain techniques or methods to look for a pattern or different form in a selected data. The technique used in this study is using data mining with the C5.0 and K-Means methods. The purpose of this study was to determine student satisfaction by using and comparing the accuracy of the C5.0 and K-Means clustering algorithms in predicting student satisfaction on lecturer performance at Universita Prima Indonesia, Faculty of Science and Technology. The results of research using the C5.0 Algorithm method where the accuracy value obtained is 90.90% (Very Satisfied) while the accuracy value is 9.10% (Not Satisfied). The K-Means Clustering method gives quite good results in classifying data, more than 75% of respondents feel (Very Satisfied) while less than 25% feel (Not Satisfied) from the teaching given by lecturers at Universitas Prima Indonesia.