PENGELOMPOKAN PERFORMA PEMAIN BASKET DENGAN SELEKSI FITUR NILAI STATISTIK MENGGUNAKAN K-MEANS DAN FUZZY C-MEANS

  • Alexander Franklyn Universitas Kristen Satya Wacana
  • Yessica Nataliani Fakultas Teknologi Informasi Universitas Kristen Satya Wacana
Keywords: basketball, players’ performance, feature selection, statistical values, k-means, fuzzy c-means

Abstract

Assessment of individual performance in an organization is needed to improve organizational performance, not least in the basketball team. Satya Wacana Saints Salatiga is a basketball team in Indonesia that competes in the Indonesian Basketball League (IBL). All players have statistical values in a basketball game, including points, assists, blocks, rebounds, and steals. These five features can be used as a reference for coaches in determining the performance of players consisting of players with good, moderate, and poor performance. Feature selection determines the feature(s) that most affects a player's performance. In this study, a feature selection of the statistical value of players was carried out to assess the performance of basketball players. The data was obtained from IBL statistical data for the Satya Wacana Saints Salatiga team for the 2021 season. The method used was k-means and fuzzy c-means with feature selection. The experiment results showed that the point value was the main factor influencing players' performance. The comparison between the k-means and fuzzy c-means with the actual performance of the coaches' assessment shows that the fuzzy c-means algorithm has an accuracy rate of 1.0000, while the k-means algorithm is 0.8000. It means that the evaluation of the player's performance can be determined from the point value using the fuzzy c-means algorithm.

Downloads

Download data is not yet available.

References

[1] R. A. Khalif, S. Andryana, and W. Winarsih, “Penilaian Pemain Basket untuk Menentukan Posisi Menggunakan Fuzzy Mamdani,” J. Inform. Merdeka Pasuruan, vol. 3, no. 3, pp. 8–14, 2018, doi: 10.37438/jimp.v3i3.181.
[2] J. C. Bezdek, A Primer on Cluster Analysis: 4 Basic Methods That (Usually) Work, 1st ed. Florida: First Edition Design Publishing, 2017.
[3] M. S. Yang and Y. Nataliani, “A Feature-Reduction Fuzzy Clustering Algorithm Based on Feature-Weighted Entropy,” IEEE Trans. Fuzzy Syst., vol. 26, no. 2, pp. 817–835, 2018, doi: 10.1109/TFUZZ.2017.2692203.
[4] Indonesian Basketball League Office, “Tentang IBL Indonesia,” 2021. https://iblindonesia.com/profile/ibl.
[5] V. Bolón-Canedo, A. Alonso-Betanzos, L. Morán-Fernández, and B. Cancela, “Feature Selection: From the Past to the Future,” in Advances in Selected Artificial Intelligence Areas. Learning and Analytics in Intelligent Systems, Springer, Cham, 2022.
[6] A. Erga and Y. Nataliani, “Seleksi Fitur pada Pengelompokan Posisi Pemain Basket menggunakan Fuzzy C-Means,” Jointecs, vol. 6, no. 2, pp. 77–84, May 2021, doi: 10.31328/JOINTECS.V6I2.2346.
[7] E. Anıl Duman, B. Sennaroğlu, and G. Tuzkaya, “A Cluster Analysis of Basketball Players for Each of the Five Traditionally Defined Positions,” Dec. 2021, doi: 10.1177/17543371211062064.
[8] M. A. Akbar, F. Fatimah, and J. Jaenudin, “Penerapan Data Mining Untuk Pengelompokan Posisi Pemain Sepak Bola Menggunakan Algoritma k-Means Clustering,” in Seminar Nasional Teknologi Informasi, 2019, pp. 278–282.
[9] E. Nasdeolta, “Klasterisasi Performa Pemain Sepakbola Liga Indonesia Menggunakan Algoritma k-Means (Studi Kasus: Indonesia Soccer Championship 2016),” UIN Sultan Syarif Kasim Riau, 2018.
[10] R. A. Siregar, “Seleksi Penyerang Utama Menggunakan k-Means Clustering dan Sistem Pendukung Keputusan Metode Topsis,” Technomedia J. (TMJ, vol. 2, no. 1, pp. 37–48, 2017.
[11] A. S. Wiyantoro, L. Junaedi, and T. M. Fahrudin, “Seleksi Fitur dan Preferensi Penyerang Terbaik Liga Inggris Berbasis Fisher’s Discriminant Ratio, k-Means Clustering dan Topsis,” J. Ilm. Inform., vol. 7, no. 2, pp. 76–81, 2019, doi: 10.33884/jif.v7i02.1328.
[12] F. A. Nanda and D. Dimyati, “The Psychological Skills of Basketball Athletes: Are There Any Differences based on The Playing Position?,” J. Keolahragaan, vol. 7, no. 1, pp. 74–82, 2019, doi: 10.21831/jk.v7i1.26360.
[13] B. Christian and L. Hakim, “Penerapan Algoritma Fuzzy C-Means pada Penentuan Lokasi Gudang Pendukung PT. XYZ,” AITI, vol. 16, no. 1, pp. 31–48, 2019, doi: 10.24246/aiti.v16i1.31-48.
[14] M. E. Celebi, Ed., Partitional Clustering Algorithms, 1st ed. Berlin: Springer, 2015.
[15] J. P. Ortega, N. N. Almanza-Ortega, A. Vega-Villalobos, and R. Pazos-Rangel, “The K-Means Algorithm Evolution,” in Clustering, Intech Open, 2019, pp. 1–22.
[16] Indonesian Basketball League Office, “Satya Wacana Saints Salatiga: Statistics,” 2021. https://iblindonesia.com/profile/team/126043?season=29040&year=2021.
Published
2022-10-29
How to Cite
Franklyn, A., & Nataliani, Y. (2022). PENGELOMPOKAN PERFORMA PEMAIN BASKET DENGAN SELEKSI FITUR NILAI STATISTIK MENGGUNAKAN K-MEANS DAN FUZZY C-MEANS. IT-Explore: Jurnal Penerapan Teknologi Informasi Dan Komunikasi, 1(3), 166-178. https://doi.org/10.24246/itexplore.v1i3.2022.pp166-178