INTEGRATION OF HIERARCHICAL CLUSTER, SELF-ORGANIZING MAPS, AND ENSEMBLE CLUSTER WITH NAÏVE BAYES CLASSIFIER FOR GROUPING CABBAGE PRODUCTION IN INDONESIA

Abstract

The purpose of this study is to evaluate and compare different clustering techniques, including hierarchical cluster analysis (using complete linkage, average linkage, and single linkage methods), Self-Organizing Maps (SOM) clustering, and ensemble clustering, within the framework of integrated cluster analysis combined with Naïve Bayes analysis, specifically applied to cabbage production in Indonesia. The data utilized in this study are on cabbage production from various districts and cities in Indonesia, obtained from the 2023 publications of the Central Statistics Agency (BPS). The variables used in this study are cabbage harvest, cabbage production, area height, and rainfall. The data size used is 157 districts/cities in Indonesia. This research is a quantitative analysis employing integrated cluster analysis combined with Naïve Bayes. Cluster analysis is used to obtain classes in each district/city. Different clustering methods, including hierarchical clustering, Self-Organizing Map (SOM), and ensemble clustering, are compared to determine the best approach for grouping districts based on cabbage production. Naïve Bayes analysis is then used to classify cabbage production in Indonesia and identify the optimal clusters. This comparison aims to find the most effective clustering method for improving grouping accuracy and understanding cabbage production patterns. The best method for classifying cabbage production in Indonesia is the ensemble clustering approach integrated with Naïve Bayes, resulting in three distinct clusters: high, medium, and low production clusters.