IMPLEMENTASI METODE DECISION TREE C4.5 UNTUK MENGANALISA MAHASISWA DROPOUT

Abstract

Data Mining is the process of extracting data from large databases to find important and useful information. Classification is one of the techniques that exist in data mining. The method used is the Decision Tree and the algorithm used is the algorithm C4.5. Decision Tree is a method that alters the facts into a decision tree that represents the rules which is easy to understand. Decision Tree is useful to explore the data and find the hidden relationship between the number of input variables and targets. The Decision Tree built will obtain rules  from a case. The purpose of this study is to classify the student data in Pembnagunan Panca Budi University to determine students subjected to dropout. Attributes used consisted of the previous school, student’s age, parent’s occupation, parent’s income, and GPA. To avoid too much branching, the attributes of income, age, and GPA are grouped together. The attribute that most influence on dropout students is the previous school. The results obtained from calculation accuracy value is calculation accuracy of 59.58% and classification error of 40.42%.