Application of Data Mining for Clustering of Foreign Tourist Visits Based on Arrival Entrance
DOI:
10.33395/sinkron.v7i1.11217Keywords:
Tourism, Covid-19, Indonesia, Data Mining, K-means, ClusteringAbstract
Indonesia is a country with unique tourist destinations from each region. The tourism sector has an impact on the Indonesian economy which can encourage economic growth and increase the country's foreign exchange from foreign tourist visits. Tourism growth in Indonesia was disrupted due to the Covid-19 pandemic with the imposition of major social restrictions which resulted in a decrease in tourist visits and the paralysis of the tourism sector. Based on the problems described above, the authors are interested in conducting research in order to classify data on foreign tourist arrivals based on the entrance of foreign tourist arrivals. This research uses data mining method and K-Means Algorithm to form 5 clusters. The 5 clusters are divided into groups of tourist entrances which are categorized as very high (C1), high (C2), moderate (C3), low (C4) and very low (C5). In forming the 5 clusters, the researchers used Ms. Excel and Rapidminer 10.1 to process data. The results of this study obtained that the tourist entrance group was categorized as very high (C1) with 1 data, high (C2) with 1 data, moderate (C3) with 1 data, low (C4) with 1 data and very low (C5). ) that is with 21 data. This study aims to provide suggestions and future considerations to the Ministry of Tourism and Creative Economy of the Republic of Indonesia (Kemenparekraf) to carry out policies so that the Indonesian tourism sector can return to normal.
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Copyright (c) 2022 Handhy Nur Prabowo, Resad Setyadi, Wahyu Adi Prabowo
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