Naïve Bayes Classifier for Journal Quartile Classification

Abstract

Classification is a process for distinguishing data classes, with the aim of being able to estimate the class of an object with unknown label. One popular method that used for classifying data is Naïve Bayes Classifier. Naïve Bayes Classifier is an approach that adopts the Bayes theorem, by combining previous knowledge with new knowledge. The advantages of this method are the simple algorithm and high accuracy. In this study, it will show the ability of Naïve Bayes Classifier to classify the quality of a journal commonly called Quartile. This study use a dataset of 1491 instances. The results show an accuracy of 71.60% and an error rate of 28.40%