Perbaikan Algoritma Naive Bayes Classifier Menggunakan Teknik Laplacian Correction

Abstract

Naïve Bayes Classifier is one of the classification algorithms in Data Mining with a good processing speed and a fairly high level of accuracy. In the classification process the Naïve Bayes Classifier adopts the Bayesian theorem to map a data against a class by taking into account the probability of the attribute data, but because the Naïve Bayes Classifier makes probability the basis for its calculations, it is certainly very risk if it is wrong. If one class that is contained in the attribute has a value of 0, this will reduce the level of accuracy of the classification process carried out by the Naïve Bayes Classifier algorithm itself, therefore in this study the Laplacian Correction technique is used as an alternative to fix the problems that are owned by the Naïve Bayes Classifier Algorithm. The result of this research is that the Laplace Correction technique has succeeded in improving the performance of the Naïve Bayes Classifier by fixing the 0 value for each attribute. The level of accuracy that is owned by the Naïve Bayes Classifier after experiencing improvements with the Laplacian correction technique is 94.44%.