Aplikasi K-Medoid dalam Regenerasi Pemain Sepak Bola

Wahyu Sa'dun Akbar; Rahmadi Yotenka; Rohmatul Fajriyah

doi:10.29313/statistika.v24i2.4611

Authors

Wahyu Sa'dun Akbar Master Program in Statistics, Universitas Islam Indonesia
Rahmadi Yotenka Undergraduate Program in Statistics, Universitas Islam Indonesia
Rohmatul Fajriyah Master Program in Statistics, Universitas Islam Indonesia

DOI:

https://doi.org/10.29313/statistika.v24i2.4611

Keywords:

Sepakbola, Gelandang, K-Medoids Clustering, Principal Component Analysis

Abstract

ABSTRAK

Sepakbola merupakan olahraga paling populer. Dalam permainan sepakbola, peran gelandang sangat penting sehingga setiap klub sepakbola terutama di Liga-Liga top Eropa perlu meregenerasi gelandang untuk kompetisi Liga di musim-musim berikutnya. Penelitian ini bertujuan untuk melakukan pencarian pemain tengah pengganti yang memiliki kesamaan karakteristik bermain menggunakan metode K-Medoid Clustering. Metode Principal Component Analysis diimplementasikan untuk mengatasi multikolinieritas dan untuk mereduksi variabel yang digunakan, sehingga didapatkan 2 komponen utama. Kedua komponen utama ini menjelaskan 72.9% variansi populasi yang ada. K-Medoid Clustering menghasilkan 2 kelompok dan berdasarkan pengukuran jarak dengan metode Euclidean didapatkan bahwa Tchouameni merupakan gelandang yang paling mirip dengan Casemiro. Hasil ini mendukung keputusan Real Madrid untuk membeli Tchouameni sebagai keputusan yang tepat.

ABSTRACT

Football is the most popular sport. In the game of football, the role of midfielders is very important so that every football club, especially in the top European leagues, needs to regenerate midfielders for league competitions in the following seasons. This research aims to search for substitute midfielders who have similar playing characteristics using the K-Medoid Clustering method. The Principal Component Analysis method was implemented to overcome multicollinearity and to reduce the variables used, so that 2 main components were obtained. These two main components explain 72.9% of the population variance. K-Medoid Clustering produced 2 groups and based on distance measurements using the Euclidean method it was found that Tchouameni was the midfielder most similar to Casemiro. These results support Real Madrid's decision to buy Tchouameni as the right decision.

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