Clustering Tourism Destinations in Denpasar City in 2023 Based on Visitor Preferences Using K-Means and DBSCAN Clustering Methods - Dharmawan, Komang Satya; Sasmita, Gusti Made Arya; Piarsa, I Nyoman (JITTER : Jurnal Ilmiah Teknologi dan Komputer; Vol 6 No 1 (2025): JITTER, Vol.6, No.1, April 2025, 2025)
In the digital era, the increasing number of tourist destinations in Denpasar City poses challenges for effective and sustainable management. This study develops a clustering system for tourist destinations in 2023 based on visitor preferences using K-Means and DBSCAN methods. Data on tourist visits and destination facilities, sourced from the Denpasar City Tourism Office, were processed through preprocessing steps, including missing data imputation and feature standardization. K-Means clusters destinations based on proximity to cluster centroids, while DBSCAN excels in detecting density-based clusters and outliers. Evaluation using the Elbow Method and Silhouette Score indicates that K-Means is more optimal for forming a specific number of clusters, with Silhouette Scores of 0.654 for foreign tourists, 0.614 for domestic tourists, and 0.579 for combined tourists. Conversely, DBSCAN performs better in handling irregular data distributions and identifying outliers, with Silhouette Scores of 0.681 for foreign tourists, 0.578 for domestic tourists, and 0.529 for combined tourists. The agreement rate between the two methods reaches 86.7% for foreign tourists, 80.0% for domestic tourists, and 83.3% for combined tourists. The results are visualized through an interactive dashboard mapping cluster distributions, enabling the Tourism Office to analyze visit patterns and formulate targeted, efficient strategies for destination development