Implementasi Data Mining Klasifikasi Mahasiswa Drop Out Dengan Metode Decision Tree Algoritma

Abstract

Academic status of students evaluated each end of the semester, if not meet the academic criteria in accordance with the applicable rules stated then broke college student or dropout (DO). This study aims to classify potential dropout students with C4.5 algorithm. The algorithm is a classification algorithm C4.5 decision tree data type (Decision Tree). C4.5 decision tree algorithm is built in three stages, namely the selection of attributes as root, create a branch for each value and dividing the cases in the branch. These stages will be repeated for each branch until all cases the branches have the same class. From the completion of the decision tree will produce some rule or knowledge of a case. In this thesis, the author classifies the potential dropout students. The classification of dropout students is largely determined by the input variables are determined, among others, GPA, study period, total credits, and non-active semester. With the implementation of the C4.5 algorithm will be able to assist parties in determining the criteria of potential dropout students.