Klasifikasi Lama Masa Studi Mahasiswa Menggunakan Perbandingan Metode Algoritma C.45 dan Algoritma Classification and Regression Tree - Hadi Dwi Darmawan; Desi Yuniarti; Yuki Novia Nasution (Program Studi Statistika FMIPA Universitas Mulawarman, 2017)
Classification is the grouping samples based on the characteristics of the similarities and differences using target variable category. In this study, the decision tree is formed using C4.5 algorithm and Classification and regression tree (CART) algorithm to classify a student’s study period class of 2016 FMIPA UNMUL. C4.5 algorithm is a non binary classification tree where the branches of trees can be more than two on C4.5 algorithm, decision tree is established based on Entropy value. The purpose of CART algorithm is to get an accurate data as group identifier of a classification. CART can be applied in three main steps, namely the establishment of a classification tree, trimming of the classification tree, and determination of optimal classification tree. The main goal of this research is to determine factors which may effect on all predicate graduation who was graduated on 2016 using C4.5 algorithm and CART algorithm and also to know comparison accuracy of classification result by C4.5 algorithm and CART algorithm. The result showed that factors which affected the duration of all graduation using C4.5 algorithm are major (X4), region school (X5) and region origin (X3) and factors affected to the duration of all graduation using CART algorithm are major (X4) and Cumulative Achievement Index (X1). Precision classification in CART algorithm is better than C4.5 algorithm. C4.5 algorithm was able to predict with 40% accuracy while the CART algorithm has a predictive accuracy of 60%.