Optimization of Village Grouping Using Comparison of K-Means and K-Medoids Methods - Eza Rahmanita; Yeni Kustiyahningsih; Putri Nihayatul Husna; Adhelia Firdaus Al-Najib (Teknika, 2025)
Sumenep Regency is the largest agricultural area in Madura and a major producer of food crops such as rice, corn, and vegetables. However, agricultural productivity in some areas has declined due to uneven fertilizer distribution and limited knowledge about plant diseases and their treatment. To address this, a village clustering system is needed to help the Agriculture Office identify areas with high and low productivity, enabling more targeted assistance and resource allocation. This study aims to classify villages based on agricultural productivity to support better decision-making in agricultural development. Two clustering methods, K-Means and K-Medoids, were applied and compared. K-Means determines cluster centers based on the average of data points, while K-Medoids selects the most representative data point within each cluster. The data used in this research is 690 datasets in 2023. Before clustering is carried out, the data is preprocessed first. Data Pre-processing steps include data transformation, label encoding, imputation, and Min-Max normalization. Cluster optimization was performed using the Sum of Squared Errors (SSE) method. The results show that K-Medoids offers more stable clustering, especially in the presence of outliers, while K-Means is more efficient in computation. The best clustering result was achieved using K-Means with five clusters (K = 5), producing the lowest SSE value of 29.8. These findings can assist local governments in prioritizing agricultural support based on village productivity profiles.