Penerapan Data Mining Untuk Pengembangan Destinasi Wisata Menggunakan Algoritma K-Means Clustering - Irzal Arief Wisky; Febria Nika Putri; Mutiana Pratiwi (Riset Sinergi Indonesia (RISINDO), 2026)
One of the strategic industries that supports community wellbeing and regional economic development is tourism. In West Sumatra's Solok Regency, Alahan Panjang is home to a wide range of natural tourist sites, each with unique characteristics and possibilities for growth. Due to differences in the number of tourists, attractions, accessibility, and amenities, it is still difficult to identify development priorities for each destination. Based on their similarities, this study uses the K-Means Clustering algorithm and the Data Mining approach to categorize tourist attractions. The study uses tourist destination data from Alahan Panjang that includes variables for tourism components and visitor statistics. To ensure a consistent range of values, the data were subjected to Min-Max normalization before clustering. Three types of tourist sites are distinguished by the clustering findings: high-priority, priority, and supporting locations. Additionally, an information system built on PHP and MySQL integrated the clustering results to assist tourism managers and municipal governments in assessing tourist potential and establishing development goals. Through data-driven and objective tourism analysis, the suggested method is anticipated to increase the effectiveness of decision-making.