Klasifikasi Predikat Kelulusan Mahasiswa Menggunakan Algoritma C4.5

Abstract

Determination of student graduation predicate based on GPA and additional requirements of timely study period for honors predicate. Currently, there is no in-depth classification to identify graduation predicate, so understanding of the patterns that influence the results is still limited. Accumulation of student graduation data can be used to find new information. This study aims to produce a decision tree classification model using the C4.5 algorithm and evaluate its accuracy in classifying student graduation predicates at UIN Raden Fatah Palembang. The data division technique used is k-fold cross validation to divide the data into training and testing data. The k value used is k = 3 in the first data test, this is based on previous tests with several k values, where k = 3 produces higher accuracy than the others. The rules formed are 242 and the attribute that influences student graduation predicate is GPA. The accuracy of the application of the C4.5 algorithm in classifying student graduation predicates is 83.31% which is included in the Good Classification category.

Keywords

• graduation predicate
• data mining
• classification
• c4.5 algorithm