Analisis Pengelompokkan Provinsi di Indonesia Berdasarkan Indikator Kinerja Sektor Peternakan Sapi Tahun 2022 - Arsa Saladine; Endita Prastyansyach; Sri Pingit Wulandari (Zoologi: Jurnal Ilmu Peternakan, Ilmu Perikanan, Ilmu Kedokteran Hewan, 2025)
Indonesia, based on natural resource potential, has great potential to achieve beef self-sufficiency. The contribution of this sector is not only limited to meeting food needs in the form of beef, but also includes economic aspects such as providing employment opportunities, industrial raw materials, and increasing the income of local farmers. This shows that the development of this sector has great potential in supporting food security and improving community welfare. Therefore, research was conducted on performance indicators that could influence the performance of the cattle farming sector in Indonesia in 2022 using cluster analysis. Cluster analysis is a statistical method that identifies groups of samples based on similar characteristics. Cluster analysis has two methods, namely hierarchical and non-hierarchical. This research focuses on classifying regions in Indonesia into groups based on similar characteristics. In this research, cluster analysis assumptions will be tested, namely the multivariate normal distribution test, conducting cluster analysis using hierarchical and non-hierarchical methods, characterizing the data in each cluster, then drawing conclusions and suggestions from the research results. Based on the research results obtained on data characteristics, it was found that variables tend to have a variety of data. Hierarchical cluster analysis uses the single linkage method which has an optimum number of clusters of 4. The highest number of cluster members is in cluster 1. Then cluster 1 shows the highest performance in the cattle farming sector. In non-hierarchical cluster analysis using the k-means method which has an optimum number of clusters of 5. The highest number of cluster members is in cluster 4. Then clusters 2, 3 and 4 show higher performance in the cattle farming sector compared to clusters 1 and 5 .