Penerapan Metode K-Means Clustering Untuk Seleksi Atlet Taekwondo Porprov
DOI:
https://doi.org/10.30865/klik.v4i3.1462Keywords:
Clustering; Data Mining; Athlete; Python; Jupyter NotebooksAbstract
With the increase in the number of data on taekwondo athletes in North Sumatra Koni, of course the data will be different. In Koni North Sumatra, the selection process for taekwondo athletes still uses Microsoft Excel. The selection process using Microsoft Excel is still not appropriate because of the risk of errors in data input and it takes a long time to compare previous data with the resulting data. The purpose of writing this final assignment is to apply the K-Means Clustering method for selection data for taekwondo athletes who took part in the Porprov event into several clusters. Therefore, the author is interested in taking the topic of applying the K-Means Clustering method for the selection of Porprov taekwondo athletes at Koni North Sumatra. Based on the Elbow Method, it can be seen that the optimal number of clusters for the K-Means method is 3 Clusters. Cluster 0 feasible category with the least number of athletes totaling 213 athletes. Cluster 1 category is not feasible which amounted to 57 athletes. Cluster 2 category is very feasible with the highest number of athletes totaling 216 athletes. This is because the number of clusters is 3 which consists of not feasible, feasible and very feasible. In accordance with the results of this research, namely by applying the K-Means Clustering method for Selection of Provincial Taekwondo Athletes in Koni North Sumatra, it was successfully implemented. The conclusion that can be drawn from this research is that using the K-Means Clustering method for selecting provincial taekwondo athletes succeeded in producing three groups (clusters) based on the characteristics contained in the data. The results of this research can also be used as a basis for developing applications as well as various research carried out in the future by making comparisons in the use of the K-Means Clustering method in an effort to group athlete data contained in KONI SUMUT
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