Penerapan Clustering dalam Mengelompokkan Jumlah Kunjungan Wisatawan Mancanegara Dengan Metode K-Means
Edy Satria, Heru Satria Tambunan, Ilham Syahputra Saragih, Irfan Sudahri Damanik, Fany Than Ervina Sitanggang · Prosiding Seminar Nasional Riset Information Science (SENARIS) · 2019
The Indonesian tourism sector currently contributes approximately 4% of the total economy. In 2019, the Indonesian Government wants to increase this figure to double to 8% of PDB (Produk Domestik Bruto), a target that implies that within the next 4 years, the number of visitors needs to be doubled to approximately 20 million tourists. This study discusses the Application of Clustering in Grouping the Number of Foreign Tourist Visits by Nationality and Month of Arrival by the K-Means Method. The source of this research data was collected based on data on the number of foreign tourist visits produced by the National Statistics Agency. K-Means clustering is one of the data mining techniques that gives a description of an item's cluster. The purpose of this study is to classify the number of foreign tourists in Indonesia. The results of this study are grouping the number of foreign tourist visits grouped by two clusters (high and low), high clusters of 4 countries and low clusters of 87 countries. Countries that are included in the lower clusters can be used for the Government of Indonesia in terms of improving existing facilities in tourist attractions so that visiting tourists will increase in the future.