Analisis Performa Pemain Basket Satya Wacana Berdasarkan Statistik Rata-Rata Pertandingan Menggunakan K-Means Clustering 31 10
DOI:
https://doi.org/10.26623/transformatika.v24i1.14334Keywords:
k-means clustering, performa pemain, basket, statistik rata-rata pertandinganAbstract
The use of match statistics data in evaluating the performance of professional basketball players in Indonesia is still limited and generally conducted descriptively. This study aims to group the performance of Satya Wacana Salatiga team players in the 2025 Indonesia Basketball League (IBL) season using the K-Means Clustering method based on average statistics per match. The research dataset consists of 16 players with 11 performance variables, namely Games Played, Minutes Played, Field Goal Percentage, Three-Point Percentage, Two-Point Percentage, Free Throw Percentage, Rebounds per Game, Assists per Game, Blocks per Game, Steals per Game, and Points per Game. The data was standardized using Z-Scores before the clustering process with three performance clusters: low, medium, and high. The results showed that one player was classified as a low-performance cluster, thirteen players in the medium-performance cluster, and two players in the high-performance cluster. The clustering results also corresponded to the team coach's evaluation, thus demonstrating that the K-Means Clustering method is capable of producing objective and practically relevant player performance segmentation. This research contributes to providing a data-driven approach to support player evaluation and decision-making in the development of professional basketball team strategies.
Downloads
References
A. A. Safitri and Priyanto, “Analisisis Statistik Pertandingan Tim Juara Basket Putra Universitas Semarang pada Pertandingan Soegijapranata Basketball League 2026,” SPRINTER: Jurnal Ilmu Olahraga, vol. 7, no. 1, pp. 277–284, Mar. 2026, doi: 10.46838/spr.v7i1.1070.
A. P. L. Sungkawa and Harwanto, “PENGARUH AKURASI JUMP SHOOT DAN LAY UP SHOOT TERHADAP KETEPATAN HASIL SHOOTING BOLA PADA CABANG OLAHRAGA BOLA BASKET,” Literacy : Jurnal Ilmiah Sosial, vol. 2, no. 1, pp. 1–7, 2020.
Aisyah Amalia, Ayu Nurani, Zulpan, and Ahmad Husein, “Konsep Statistik dan Jenis-Jenisnya,” Journal of Literature Review, vol. 1, no. 2, pp. 821–828, Dec. 2025, doi: 10.63822/nb242j16.
B. Asdianto and Andi, Basketik: Basket dan Statistik: Acuan dan Pacuan. Andi, Yogyakarta, 2022.
T. Decroos, J. Van Haaren, L. Bransen, and J. Davis, “Actions speak louder than goals: Valuing player actions in soccer,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Jul. 2019, pp. 1851–1861. doi: 10.1145/3292500.3330758.
B. Asdianto, Pengantar Analisis Data Olahraga. 2019.
A. T. Wibowo, “Gaya hidup , kebugaran,” Sportif, vol. 2, no. 2, pp. 76–84, 2016.
L. M. Rahma, “Implementasi Algoritma K-Means Berbasis Android Untuk Clustering Tingkat Pemahaman Mahasiswa,” Undergraduate Tesis, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, 2020.
S. P. Lloyd, “Least Squares Quantization in PCM,” IEEE Trans. Inf. Theory, vol. 28, no. 2, pp. 129–137, 1982, doi: 10.1109/TIT.1982.1056489.
J. Smith, Clustering Aplication in Sport Data. 2023.
F. Gregorry and Y. Nataliani, “Clustering Performa Pemain Basket Berdasarkan Posisi dan Statistik Pemain Menggunakan Metode Fuzzy c-Means,” Transformatika, vol. 15, no. 1, 2022.
A. Erga and Y. Nataliani, “Seleksi Fitur pada Pengelompokan Posisi Pemain Basket menggunakan Fuzzy C-Means,” JOINTECS (Journal of Information Technology and Computer Science), vol. 6, no. 2, p. 77, 2021, doi: 10.31328/jointecs.v6i2.2346.
A. Franklyn and Y. Nataliani, “PENGELOMPOKAN PERFORMA PEMAIN BASKET DENGAN SELEKSI FITUR NILAI STATISTIK MENGGUNAKAN K-MEANS DAN FUZZY C-MEANS,” IT-EXPLORE, vol. 1, no. 3, pp. 166-178, 2022.
P. Putriana et al., “ANALISIS CLUSTERING PRESTASI ATLET PADA BERBAGAI CABANG OLAHRAGA MENGGUNAKAN ALGORITMA K-MEANS (STUDI KASUS: DINAS PEMUDA DAN OLAHRAGA KABUPATEN CIREBON),” JATI: Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 6, 2023.
M. Tria Audina Gultom and R. Amanda Putri, “Penerapan Metode K-Means Clustering Untuk Seleksi Atlet Taekwondo Porprov,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 3, pp. 1539–1550, 2023, doi: 10.30865/klik.v4i3.1462.
A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognit. Lett., vol. 31, no. 8, pp. 651–666, Jun. 2010, doi: 10.1016/j.patrec.2009.09.011.
S. Fu, S. Y. Lu, D. L. Davies, and D. W. Bouldin, “The string-to-string correction problem,” Journal of the ACM (JACM), vol. 21, no. 1, pp. 168-173, 1977.
P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53-65, 1987.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Mas Kahono Alif Bintang Firmansyah, Yessica Nataliani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Transformatika is licensed under a Creative Commons Attribution 4.0 International License.
