Klasterisasi Kebutuhan Pupuk Bersubsidi Menggunakan Algoritma K-Means dan Elbow Method - Nurya Herlina Sari; R.Rhoedy Setiawan; Yudie Irawan (Institut Teknologi Garut, 2026)
The distribution of subsidized fertilizer at the UD Barokah Tani Kiosk in Pati Regency does not yet meet farmers’ needs due to the manual management of RDKK data. This study aims to cluster subsidized fertilizer needs using the K-Means algorithm, validated by the Elbow Method and Silhouette Score. The data used consists of 1,420 RDKK records for the 2025–2026 period, with variables including land area, UREA_TOTAL, NPK_TOTAL, and the number of commodities. The results indicate that the optimal number of clusters is k = 3, with a Silhouette Score of 0.9192, indicating very high cluster quality. The data is divided into three categories: low, medium, and high, with a dominance in the low to medium categories. This study contributes by comprehensively integrating fertilizer requirement variables and using a combination of the Elbow Method and Silhouette Score to enhance the validity of the clustering results. The clustering results are implemented in a web-based system to support rapid, data-driven analysis and visualization.