PERBANDINGAN K-MEDOIDS DAN CLARA (Clustering Large Application) PADA DATA POPULASI TERNAK DI INDONESIA

This study compares the K-Medoids and CLARA (Clustering Large Application) methods for livestock population data in Indonesian districts and cities. Calculating the distance between points and objects in the data, K-Medoids is a method for clustering based on data points (medoids). A larger dataset is divided into several samples for comparison in CLARA, an extension of the K-Medoids approach. The CLARA method analysis results show that three clusters are the ideal number. The ideal number of clusters in a K-Medoids cluster analysis is two. The Silhouette Score (SS), Davis-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI) are the metrics that are measured. The evaluation of the comparison results shows that the CLARA method has an SS value of 0.66, while K-Medoids has an SS value of 0.62. The comparison of the CLARA and K-Medoids approaches yielded DBI values of 1.38 and 1.92, respectively, and 197.54 and 132.73 for CHI. The findings indicate that, in comparison to the K-Medoids approach, the SS value for the CLARA method is closer to 1, and that the CHI value derived from the CLARA method is likewise greater. The K-Medoids approach has a higher DBI value than the CLARA method, where a lower DBI value denotes superior performance. The CLARA approach is the most effective way to do cluster analysis on livestock population data in Indonesian districts and cities, according to the findings.