Open AccessPrediksi Mahasiswa Berpotensi Non Aktif Menggunakan Data Mining dalam Decision Tree dan Algoritma C4.5
Author(s) -
Nur Yanti Lumban Gaol
Publication year - 2020
Publication title -
jurnal informasi dan teknologi
Language(s) - English
Resource type - Journals
ISSN - 2714-9730
DOI - 10.37034/jidt.v2i1.22
Subject(s) - decision tree , computer science , data mining , mathematics education , medical education , psychology , medicine
Non-active students are students who do not attend the lecture process and do not pay tuition administration fees within two semesters or more. Reports on students who are not active will have an impact on the quantity of tertiary institutions. Students who are not registered in non-active students will potentially be expelled or dropped out. For this reason, this research was conducted to explore information on potentially non-active students by applying data mining science with the Decision Tree method and C4.5 algorithm. The tested data were sourced from Triguna Dharma Medan College of Information and Computer Management (STMIK). The results of the study get prediction rules for student data that are potentially non-active with a very good degree of accuracy. So this research can be used to avoid students dropping out unilaterally.