Naïve Bayes Classifier for Journal Quartile Classification
Aji Prasetya Wibawa, Ahmad Chandra Kurniawan, Della Murbarani Prawidya Murti, Risky Perdana Adiperkasa, Sandika Maulana Putra, Sulton Aji Kurniawan, Youngga Rega Nugraha · International Journal of Recent Contributions from Engineering Science & IT (iJES) · 2019
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%