Integration of K-Means Clustering and Elbow Method for Mapping Baccaurea spp. Distribution to Support Agroindustrial Development in West Sulawesi

Baccaurea spp. is a type of wild plant with potential value for sustainable agroindustrial development. This study aims to map and segment regions in West Sulawesi based on the habitat suitability for Baccaurea spp. using K-Means Clustering integrated with the Elbow Method. Field data were collected from 25 villages in Mamasa and Mamuju districts, involving five parameters: land area, production estimate, altitude, humidity, and average temperature. Based on the results of the exploration, 3 species of Baccaurea spp. have been found, namely Baccaurea Lanceolata, Baccaurea Costulata, and Baccaurea Racemosa. The analysis yielded three clusters, with Cluster 1 being identified as the top priority for agroindustrial development due to its high productivity and optimal land conditions. The findings provide a data-driven foundation for policymakers and industries to support the sustainable cultivation of Baccaurea spp. in Indonesia. This research contributes to informatics-based decision-making in agroindustry development and regional planning.