PENERAPAN KLASTERISASI DENGAN MENGGUNKAN ALGORITMA K-MEANS UNTUK MENGETAHUI TINGKAT KUNJUNGAN WISATA DI BOJONEGORO

Devi Dwi Nuraliza, Ita Aristia Sa’ida

Abstract


Bojonegoro is one of the regencies in East Java that has a tourist attraction that is in demand by tourists, tourist visits in Bojonegoro can increase the country's foreign exchange and improve the people's economy. The addition of tourism potential can affect the number of visits which leads to a reduction in visits to several tourist sites. However, the Covid-19 pandemic has had a fairly serious impact on the Indonesian economy, especially in the tourism sector so that the growth in the rate of tourist visits in Bojonegoro from the amount of data every month and year is still very difficult to know. Clustering of tourist visits needs to be done to find out the clusters of the highest level of object visits to the lowest visits so that improvements can be made to facilities and infrastructure of superior objects that can increase the number of visits and have an impact on increasing State Foreign Exchange. The clustering method uses the K-Means algorithm with distance calculations using Euclidean distance. The data source comes from the Department of Culture and Tourism of Bojonegoro Regency. The research data used is the number of tourist visits in 2016-2020. The data are grouped into 3 clusters of tourist visits, namely high, medium and low. The results of the Euclidean Distance calculation are three (3) iterations with a fixed cluster of 16 and a cluster changing of 0. Clustering in 2016, 2017 and 2018 the highest tourist visit value (C1) is 1. Moderate tourist visit (C2) is 1 and tourist visit low (C3) as many as 14. The results of the Euclidean Distance calculation in 2019 obtained the highest visit value (C1) as much as 1, moderate tourist visits (C2) as many as 3 and low tourist visits (C3) as many as 12. While the results of the Euclidean Distance calculation in 2020 obtained The highest tourist visit value (C1) is 3. Moderate tourist visits (C2) are 2. While low tourist visits (C3) are 11

Keywords


Tourist Visits, Clustering, K-Means Algorithm

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DOI: https://doi.org/10.33758/mbi.v16i5.1583

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