HYBRIDIZATION OF THE NAIVE BAYES CLASSIFICATION METHOD IN THE FRESHWATER FISH SEED SELLER CLASSIFICATION MODEL

Abstract

Freshwater fish seed sellers play several roles in the supply chain process in the freshwater fish farming business. The role of the seller of freshwater fish seeds in this process is to distribute fish seeds which are one of the upstream sources in the supply chain process. Freshwater fish cultivators must select competent freshwater fish seed sellers so the supply chain process can run well. A large number of freshwater fish seed sellers in the market remind freshwater fish cultivators to choose the quality of the freshwater fish seed seller in terms of seed quality, low prices, shipping that can reach many areas, ergonomic packaging, and others. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for classification. This study aimed to compare the seed seller classification method in which the appropriate pattern of seed seller was identified by hybridization of Naïve Bayes Classifiers (NBCs), and then the researchers conducted performance appraisal and evaluation. The results are beneficial for freshwater fish cultivators and researchers which will enable them to formulate their plans according to the predicted results. The proposed method has produced significant results by achieving a training data accuracy of 82.61% and the testing data accuracy of 73.91%.