Analisis Segmentasi Kunjungan Wisata Menggunakan Algoritma K-Means pada Dinas Pariwisata Kutai Kartanegara - Rully Wijaya Saputra; Nursobah; Ahmad Abul Khair (LPPM Universitas Bhinneka Nusantara, 2026)
Kutai Kartanegara Regency possesses abundant and diverse tourism potential that can contribute significantly to regional economic development. Nevertheless, tourism visitation data has generally been utilized only for administrative documentation and reporting purposes, limiting its role in supporting strategic decision-making. Therefore, a data-driven approach is required to identify the characteristics and performance of tourism destinations more effectively. This study aims to analyze and segment tourism destinations in Kutai Kartanegara Regency using the K-Means Clustering algorithm. The research employed secondary data obtained from the Kutai Kartanegara Tourism Office covering the period from 2023 to 2025. Three variables were used in the clustering process, namely total domestic tourist visits (Wisnus), total international tourist visits (Wisman), and seasonal visitation fluctuations. Prior to clustering, the data underwent preprocessing and normalization to improve clustering performance. The Elbow Method was applied to determine an appropriate number of clusters, resulting in a three-cluster solution representing high-, medium-, and low-visitation categories. The clustering process successfully grouped 56 tourism destinations into 28 destinations in the medium-visitation cluster, 12 destinations in the high-visitation cluster, and 16 destinations in the low-visitation cluster. Furthermore, model evaluation using the Silhouette Score produced a value of 0.4288, indicating a moderate and acceptable level of cluster quality. The findings provide a comprehensive overview of tourism destination performance and can support policymakers in prioritizing destination development, improving resource allocation, and formulating more targeted tourism promotion strategies to enhance regional tourism competitiveness.