Analysis of Clusters Number Effect Based on K-Means Method for Tourist Attractions Segmentation

Abstract

The current development of tourism potential can have an impact on the implementation of regional autonomy. Madura is an island located at the northern tip of East Java Province, which has tourist attractions spread over four regencies, namely Bangkalan, Sampang, Pamekasan, and Sumenep. However, there are still many attractions that tourists have not touched. Therefore, the Madurese government tries to develop the existing potential. However, the development of this potential has not been carried out evenly by the government. This is due to the lack of grouping of data on the number of tourists, making it difficult for the tourism office to analyze the dominant visitor effect based on the variables that become characteristics in mapping the entrance ticket to increase the potential of tourism objects in Madura. There are 21 tourist objects in Bangkalan. There are several influencing factors to mapping, such as: Gender, age, occupation, education, and marital status. In this study, the admission ticket mapping system applies the K-Means method by dividing tourist objects into three groups including, (C1) high cluster, (C2) medium set, and (C3) low cluster. K-Means functions to map objects by mapping the analysis of data mining applications that can increase the number of tourist visits that impact the introduction of tourist objects and improve the country's foreign exchange through the development of facilities and infrastructure.