Regional Tourism Development in Nusa Tenggara Barat: Maximizing Local Economic Development

Abstract

The diversity of each region causes different potentials in each region. The potential of the village can map how rich the area is, the advantages of the area, and the population and welfare. Tourism is one of them; this sector is potential for the area because it can lift its economy if it is adequately managed. Good management is born from the policies/regulations of the local government. Nusa Tenggara Barat is a province with many tourist attractions. However, from an economic and socio-cultural perspective, Nusa Tenggara Barat has yet to be able to compete with other major provinces in Indonesia, such as the Special Region of Yogyakarta (DIY). The 2018 Village Potential Data by BPS can assist the government in compiling efforts for the village's progress. In the process of data processing, especially big data, in-depth exploration is needed to produce meaningful insight. Clustering is one of the exploration techniques that can map areas in Nusa Tenggara Barat based on the tourism potential in each village. K-Prototypes are used in cases with mixed variables (numeric and categorical). Determination of the best number of clusters is using the silhouette index. It produced 5 clusters with their respective diversity. There are five clusters in Nusa Tenggara Barat by the villages based on tourism aspects and factors that support tourism. Cluster 3 is an ideal cluster, meaning tourism development in that cluster is complete. Cluster 5 has considerable potential in tourism because the supporting factors are analytically good. There are villages dispersed across Sumbawa Barat, Sumbawa, Lombok Tengah, Lombok Barat, Dompu, and Bima that are part of cluster 1. In Sumbawa Barat and Lombok Tengah, cluster 1 predominates numbers. The settlements in cluster 2 are then more prevalent in Sumbawa and Bima. Furthermore, Sumbawa, Dompu, and Bima have the highest concentrations of cluster 4. Unlike clusters 3 and 5, special attention should be paid to clusters 1, 2, and 4 in tourism development. Implications of this research are the government could take toward each cluster to increase the GDP-oriented service product, namely tourism; whether it is an improvement or reconstruction, clustering analysis works its role in learning the data to make the policy more focused.