Analisis Ternak Menggunakan K-Means Clustering Dalam Business Intelligence - Sulaiman Savero Sagi; Safrizal Safrizal (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
This study aims to analyze livestock population patterns and classify regions in Central Java using data from 2020–2023 covering cattle, goats, and chickens from official sources. The Knowledge Discovery in Database (KDD) framework and K-Means Clustering were applied, with the optimal number of clusters determined using the Elbow Method and Silhouette Score. The results show that the optimal number of clusters is two (K=2), with a Silhouette Score of 0.328, indicating a relatively weak clustering structure with potential overlap. Despite this limitation, the results reveal meaningful segmentation when combined with Business Intelligence analysis. Cluster 0 represents regions with lower population but higher growth, while Cluster 1 represents regions with higher population but lower or negative growth. Further analysis indicates that the relationship between population, growth, and production is not linear, where high production does not necessarily correspond to strong growth. These findings highlight the importance of distinguishing between current production capacity and future growth potential, providing more informative insights for data-driven decision-making in livestock sector management.