Comparison of Student Graduation Classification Analysis Based on Study Length Using Naïve-Bayes and C4.5 Algorithms

Data mining is the process of finding patterns and knowledge from large number of data. An important part of data mining is data classification. Classification is used to classify data based on the nature of the data that each class has recognized. There are various techniques used to classify data, two of which are C4.5 and Naive Bayes. C4.5 is an algorithm used to form a decision tree while Naive Bayes is a classification method using the Bayes theorem. Based on some researchers, the C4.5 and Naive Bayes methods have good performance so the system was made with the aim to compare the performance of C4.5 and Naive Bayes. The data that will be used in this study are academic students of one of the state universities in Indonesia. Based on the results of research using WEKA 3.9.3 tools it is known that the performance of the C4.5 algorithm is better than the performance of the Naive Bayes algorithm. This can be seen from the value of accuracy, recall, precision produced by C4.5 is greater than that of Naive Bayes.