Data Optimization using PSO and K-Means Algorithm

Abstract

Tourism is one of the industries that contribute considerably to the country's economy. Tourism helps the country's economy expand by providing and increasing jobs, living standards, and triggering the rise of other tourist-related production. The tourism industry will become a multinational industry and the primary driver of the global economy in the twenty-first century. Tourism has generated significant foreign exchange for a number of countries. Indonesia, the world's biggest archipelagic country with 17,508 islands, often known as the archipelago or maritime country, has recognized the importance of the tourist sector to the Indonesian economy because tourism growth consistently outpaces economic growth. The research's goal is to map the number of tourist visits. The mapping is in the form of clusters based on countries. The technology utilized is classification data mining with the K-Means method and Particle Swarm Optimization (PSO). The dataset came from the Central Bureau of Statistics, a government organization (abbreviated as BPS). The research outcomes in cluster mapping, with the cluster results compared to standard K-Means and K-Means + PSO. RapidMiner software is used during the analytical process. The calculation results in the form of clusters will be evaluated using the Davies-Bouldin Index (DBI) parameter. The cluster value (k) used is k = 2, 3, 4, 5. The findings show that the K-Means + PSO optimization has the minimum DBI value for k = 5. Meanwhile, the DBI value for k = 5 is 0.134.