Implementation of the K-Means Clustering Method for Selecting Pencak Silat Athletes at the North Sumatra KONI

This article discusses the application of the K-Means clustering method in the selection of pencak silat athletes at the North Sumatra Koni. The study aims to improve the efficiency of the selection process, which was previously carried out manually, by classifying athletes based on attributes such as age, weight, and physical ability. K-Means clustering is used to group athletes into three categories: very suitable, suitable, and unsuitable. The methodology includes determining the number of clusters, calculating centroids, and analyzing Euclidean distances for data clustering. The results of the study showed that this method was effective in classifying 394 athletes in the "unsuitable" category, 354 athletes in the "suitable" category, and 376 athletes in the "very suitable" category. This study is expected to support the athlete selection process more systematically and efficiently. Recommendations for further research include the use of additional criteria and exploration of other clustering methods for more optimal results.