Clustering Villages in the Mountain Areas in West Java Based on Tourism Potential Using K-Prototype Algorithm
DOI:
https://doi.org/10.36456/jstat.vol16.no2.a8167Keywords:
Cluster Analysis, K-Prototype, Tourism PotentialAbstract
Cluster analysis is a multivariate analysis method used to group objects based on their similar characteristics. In general, in the clustering process only use numerical or categorical data. But, sometimes we also encounter cases that use both numerical and categorical data. Therefore, the algorithm that can be used is K-Prototype. K-Prototype is a development of K-Means that can be used on large data with numerical and categorical types. The basis of K-Prototype development is to measure the distance between the object and its centroid prototype. The number of prototypes depends on the number of clusters formed. In this study, researchers use this algorithm to group mountainous villages in West Java based on their tourism potential, in order to find out the potential that needs to be developed based on five components, namely Attractions, Access, Accommodation, Amenities and Awareness. Based on the Silhouette and McClain Index, the optimal number of clusters is two. Cluster 1 consists of 103 villages and cluster 2 consists of 703 villages. Cluster 1 are villages that are generally better in the Access, Awareness and Amenities, but are still lacking in the Attraction and Accommodation components compared to villages in cluster 2.
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