Enhancing Tourist Experiences in North Toraja through K-Means Clustering-Based Recommendation System - Srivan Palelleng; Eko Suripto Pasinggi; Juprianus Rusman (INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, 2026)
Background: North Toraja in South Sulawesi, Indonesia, is a culturally rich region with high tourism potential due to its unique traditions. The government has invested in infrastructure to boost tourism and regional income (PAD), but has insufficiently used information systems for promotion. An innovative system that can assist tourists in navigating the diverse attractions in North Toraja based on their interests needs to be developed. Objective: This research aims to develop a recommendation system for tourist attractions in North Toraja using K-means Clustering and the Similar Characteristics Method. Methods: We used Orange Data Mining to perform K-means clustering, and then used similarity-based methods to determine the closeness of characteristics among attractions. The system analyzes based on the fields of cultural, geographical, facility, and landscape features, resulting in four distinct clusters. The clusters were defined as three tourist attractions in cluster C1, eleven in C2, four in C3, and fourteen in C4. We also developed a system interface that allows travelers to input preferences, view personalized recommendations, and access detailed information. The system's novelty lies in its specific application of K-Means Clustering to leverage these local attributes for granular categorization for effective promotion of North Toraja's diversity. Conclusion: Our approach effectively groups attractions with similar characteristics, enhancing exploration based on user interests. The high altitude and similar geographical features of North Toraja result in attractions that share natural characteristics, making this system an advancement in technology-driven tourism solutions.