<![CDATA[Indonesia is one of the countries with a large livestock population. A healthy and stable livestock population can affect the production and availability of livestock products, such as meat, milk, eggs, and skin. FAO's Domestic Animal Diversity - Information System (DAD-IS) data (2020) recorded around 206 large farms, small farms, poultry and pigs. Clustering is a technique for grouping data without unknown class labels. Clustering is used to find data that has similarities. The clustering technique is to determine the initial cluster center. This study is intended to determine the best cluster value using the selected method. The purpose of this study is to create a system that can process and group data. With data obtained from the central statistics agency. This study uses the topic of Livestock Population Map in Indonesia using K-Medoids. The algorithm used in this study is K-Medoids. The K-Medoids method is a variation of the K-Means method to retrieve k data, the number of clusters in a data set with n objects. There are several processes carried out in this study including collecting data, then entering the preprocessing stage, grouping data that has similarities between data. After clustering using K-Medoids, it was found that Cluster 0 had 3 provinces with the highest average population with types of livestock such as Dairy cattle, Beef cattle, Sheep and Goats, Cluster 1 had 29 provinces with the lowest average population, Cluster 2 had 2 provinces with the highest average number for types of livestock such as Buffalo, Horse and Pig.]]>
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