Hierarchical Clustering for Rice Planting Season Recommendations in Subak Tabanan - Dewi, Ni Made Cahyani; Hidayat, Ahmad Tri (Journal of Scientific Research, Education, and Technology (JSRET), 2025)
This research applies hierarchical clustering to classify rice harvest periods in Subak Tabanan, Bali, using monthly harvest area data from 2020–2024. The objective is to identify seasonal patterns that can guide planting recommendations for local farmers. Data preprocessing involved standardization and transformation into numerical format suitable for clustering. The analysis focused on three clusters representing rainy season, transitional season, and dry season. The results indicate that most months fall within the rainy season cluster, while transitional months and a single dry month were distinctly identified. The silhouette score value shows moderate clustering performance, indicating that hierarchical clustering is capable of distinguishing planting seasons effectively. Visualization through dendrogram and distribution charts supported the identification of cluster groups. This study contributes to agricultural decision support systems, particularly in improving planting strategies and ensuring rice production sustainability in Subak Tabanan.