KLASIFIKASI DATA MINING MENGGUNAKAN NAÏVE BAYES CLASSIFIER DENGAN ALGORITMA C5.0

(Classification Data Mining using Naïve Bayes Classifier with C5.0 Algorithm)

Abstract

Data mining is a process of detecting interesting patterns and knowledge from large amounts of data. Data mining has several tasks, one of them is classification. Classification is a process of grouping data into certain classes based on the variables. There are various methods to complete classification. The method that is often used for classification is Naïve Bayes Classifier (NBC). This is because NBC is considered an easy and efficient method. NBC is a combination of naïve (the condition between variables are assumed to be independent) and Bayes theorem. The assumption of independent variables in NBC can sometimes result in unfavorable results in classification. This can be avoided by adding the C5.0 algorithm to NBC. C5.0 algorithm is an algorithm that is useful for selecting variables based on information gain value. The algorithm is run before classifying with NBC. This study discusses about theory of classification using NBC with C5.0 algorithm. C5.0 algorithm added to NBC can optimize classification and know the most influential variables.

Keywords: C5.0 Algorithm, Classification, Data Mining, Naïve Bayes Classifier.