A combination of algorithm agglomerative hierarchical cluster (AHC) and K-means for clustering tourism in Madura-Indonesia

Abstract

The development approach through the tourism sector is one of the programs launched by the government since 2016. However, the development approach is not carried out in all areas because the number of accommodation and public facilities is minimal and uneven, one of which is in Madura. With so many tourist objects in Madura, it is necessary to distribute the development of public facilities and analyze tourism that has a non-strategic distance to public facilities to help increase tourist visits. This study builds a system for clustering tourist attractions in each district in Madura based on the distance to public facilities which include hotels, gas stations, restaurants, and mosques which are important criteria and considerations for tourists in visiting a tourist location. The method used in this research is a combination of the AHC method with K-Means. The test results of the AHC, K-Means method, and the combination of AHC and K-Means methods using the Silhouette Coefficient method indicate that the AHC and K-Means combination method is the best method with a Silhouette Coefficient value of 0.8055 for k=2 and is classified as a strong structure, for the K method. -Means produces the highest Silhouette Coefficient value of 0.638. While the AHC 2 ROCHMAN, KHOZAIMI, SUZANTI, HUSNI, JANNAH, KHOTIMAH, RACHMAD method produces the highest Silhouette Coefficient value of 0.707.