Decision Tree C4.5 dengan Teknik Information Gain Untuk Klasifikasi Pemilihan Program Studi Tingkat Lanjut
-
Teddy Yogi Pratama
*
Universitas Islam Negeri Sumatera Utara, Medan,
Indonesia
- Armansyah Armansyah Universitas Islam Negeri Sumatera Utara, Medan, Indonesia (*) Corresponding Author
Abstract
The aim of this research is to analyze the application of informative features, classify data based on academic features, interests and talents with Information Techniques using Decision Tree C4.5. The aim of this research is to conduct research on students in determining the choice of study program to continue their education to college, because in choosing a study program to continue their education to college, students often experience difficulties in determining which study program they will choose. The research collected 140 student data, by distributing questionnaires to prospective new students and asking the school for students' academic scores, the author has 140 data that will be used in this research. Next, from the 140 data, researchers will divide it into two parts, namely 118 training data and 22 testing data to meet the needs in designing the model. Based on the results of research conducted using the Supervised Learning Decision Tree C4.5 approach and applying the Information Gain technique for classification of advanced study program selection, an accuracy of 86% was obtained. This success rate shows that the method is effective in identifying and classifying advanced study programs. This indicates that the use of Decision Tree C4.5 which utilizes the Information Gain technique has great potential as a model that can assist students in choosing their advanced study program with a satisfactory level of accuracy. With high accuracy results, this method can be relied on to provide accurate predictions in the context of study program selection.
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