Analisis Perbandingan Klasifikasi dengan Metode Decision Tree dan Naive Bayes

Abstract

Data mining is a process to discover useful information from a collection of large databases. One of the techniques that exist in data mining is classification. By applying classification techniques on student satisfaction questionnaire, will be expected to produce a certain pattern. The method used is the method of Decision Tree and Naive Bayesian and algorithms that are used to form the ID3 decision tree algorithm. Decision Tree method is a method that changes the fact that a very large into a decision tree which represents the rules. This decision tree is also useful for exploring the data, and find hidden relationships among a number of candidate input variables to a target variable. While Naive Bayes algorithm is one of the methods on probabilistic reasoning. Naive Bayes algorithm aims to classification the data in a particular class then the pattern can be used to estimate the specific classification. In this study, Decision Tree method has a higher degree of accuracy than the methods of Naive Bayes seen from the comparison of the data by using techniques Correctly and incorrectly, MSE, RMSE, MAE, RAE.