IMPLEMENTASI K-MEANS CLUSTERING MENGGUNAKAN RAPIDMINER DALAM PENGELOMPOKAN DATA KUNJUNGAN WISATAWAN ASING DI PROVINSI JAWA TIMUR: K-Means Clustering - Edwin Wira Liyanto; Ahmad Homaidi; Ahmad Lutfi (Universitas Muhammadiyah Gresik, 2024)
Tourism is one sector that is able to make a very significant contribution to the economy of a country, especially good tourism management can have a positive impact on the social and economy of a region. East Java is one of the natural tourist destination areas that attracts quite a lot of foreign tourists with its tourist attractions, traditions, culture and local wisdom which are the main targets for foreign tourist visits. Tourist grouping is carried out using Data Mining techniques applying the k-means algorithm, the analysis used uses the Knowledge Discovery in Database (KDD) method. By applying the K-Means clustering algorithm in order to cluster data on Foreign Tourist Visits (ASIA) to East Java via Juanda Airport for 2016 - 2023 taken from the official BPS East Java portal. From the data that has been processed, 2 clusters were found which produced the most optimal dbi value, namely 0.293 with cluster 0 containing 2 data, cluster 1 48 data.