Application of the K-Means Algorithm in Grouping PPLP Sports Athletes in West Java
DOI:
https://doi.org/10.31253/algor.v4i2.1877Keywords:
PPLP Athletes, K-Means, ClusteringAbstract
The Student Education and Training Center (PPLP) is a program from the Ministry of Youth and Sports which aims to foster and train young athletes in various sports. PPLP has existed in West Java since 1992. The K-Means algorithm is used to overcome difficulties in grouping regions based on the number of PPLP athlete participants each year in finding the best group. The K-Means algorithm is an unsupervised learning method that is used to group unlabeled datasets into different groups. In this study, the RapidMiner application was used and data was taken from West Java Open Data which has 243 data from 2013 to 2021. Based on the results of the analysis, it was identified that there were 5 groups of athletes who were differentiated based on the number of athletes. Group 0 is classified as a low cluster, group 1 is classified as a moderate cluster, group 2 is classified as a high cluster, group 3 is classified as a very low cluster, and group 4 is classified as a very high cluster. Each group has an unequal number of athletes and has a unequal territory. In this study, 10 tests were carried out using various K values, and optimal results were identified at K of 5 with a Davies Bouldin Index of 0.958.
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