Comparison of Naïve Bayes Classification Methods Without and With Kernel Density Estimation

Abstract

Halal certification is important to give confidence to Muslim consumers around the world regarding the halalness of products. The Halal Product Assurance Organizing Body (BPJPH) is the official auditor in Indonesia that is responsible for the halal certification process. This study aims to address the need for verification and validation of data for halal certification applications in Indonesia by using the data science approach and machine learning technology. In this study, the Naïve Bayes classification method was used to optimize the data verification and validation process. However, this method needs to be improved by applying optimization methods such as Kernel Density Estimation (KDE) to improve classification results. The results showed that the Naïve Bayes classification method with KDE optimization produced better performance than the Naïve Bayes method without optimization. The performance of the Naïve Bayes classification model without optimization achieves 87.6% Accuracy, 85.4% Recall, 88.8% Precision, and 87.1% F_measure. Meanwhile, the Naïve Bayes classification model with KDE optimization achieves 97.5% Accuracy, 95.9% Recall, 98.9% Precision, and 97.8% F_measure. Thus, it can be concluded that the Naïve Bayes classification algorithm with KDE optimization results in a performance increase of 9.9% compared to the Naïve Bayes method without optimization. This research has important implications in handling complex and non-normally distributed data and providing solutions for BPJPH in the process of verifying halal certification.