Implementasi K-means Clustering Dalam Pengelompokan Data Kunjungan Wisatawan Asing Di Indonesia
Year: 2025
Type: article
Abstract: Clustering is a data mining technique used for grouping data based on specific similarities. This study implements K-Means Clustering to analyze foreign tourist visit data in Indonesia in 2024. Using the Knowledge Discovery in Database (KDD) methodology, the research involves five stages: Data Selection, preprocessing, Transformation, data mining, and Evaluation. Data Clustering was conducted using RapidMiner software, experimenting with different cluster counts (k=2 to k=7) to determine the optimal number of clusters. Results indicate that three clusters (k=3) with the smallest Davies-Bouldin Index (DBI) value were optimal. This Clustering approach categorizes tourists into low, medium, and high visit groups, assisting policymakers in strategic tourism development. The findings support capacity planning and seasonal marketing strategies to optimize Indonesia's tourism sector.
Source: Jurnal Ilmiah Multidisiplin Ilmu
DOI: 10.69714/3hhfj353
Authors: Miftahul Arif Aldi, Zaehol Fatah
Institution: Universitas Ibrahimy
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4407063898