PREDIKSI CALON MAHASISWA BARU MENGUNAKAN METODE KLASIFIKASI DECISION TREE

• Main Authors: Mambang, Finki Dona Marleny
• Format: Article
• Language: Indonesian
• Published: LPPM Universitas Potensi Utama 2015-02-01
• Series: CSRID Journal
• Subjects: Decision Tree; C4.5; Recall
• Online Access: http://csrid.potensi-utama.ac.id/ojs/index.php/CSRID/article/view/65

Prior to the organization of health education begin the new school year, then the first step will be carried out selection of new admissions from general secondary education graduates and vocational. In this study, predicting new students to take multiple data attributes. The model is a decision tree classification prediction method to create a tree consisting of a root node, internal nodes and terminal nodes. While the root node and internal nodes are variables / features, the terminal node. Based on the experimental results and evaluations are done, it can be concluded that algorithm C4.5 with 80.39% accuracy obtained Uncertainty, Precision 94.44%, Recall of 75.00 % while the C4.5 algorithm with Information Gain Accuracy Ratio 88.24%, 98.28% Precision, 83.82% Recall.