Deteksi Gerakan Servis Bulutangkis Menggunakan Metode K-Nearest Neighbors (KNN) Berbasis Komputer Vision
DOI:
https://doi.org/10.29407/ygyvja37Abstract
Penelitian ini bertujuan mengembangkan sistem untuk mendeteksi gerakan servis dalam olahraga bulutangkis dengan memakai metode penglihatan komputer dan klasifikasi K-Nearest Neighbors (KNN). Pemilihan tema ini didasari oleh signifikansi analisis gerakan dalam meningkatkan kinerja atlet serta mendukung program pelatihan yang efisien. Servis yang tepat adalah aspek penting dalam bulutangkis. Metodologi penelitian mencakup pengumpulan data video, pengambilan fitur titik kunci tubuh dengan menggunakan kerangka kerja computer vision seperti MediaPipe, dan selanjutnya, penerapan metode K-Nearest Neighbors (KNN) untuk mengklasifikasikan fase-fase gerakan servis berdasar data titik kunci yang dihasilkan. Temuan penelitian mengindikasikan bahwa sistem dapat mengenali dan mendeteksi fase-fase utama dalam gerakan servis bulutangkis dengan ketepatan yang baik menggunakan klasifikasi KNN. Data titik kunci yang dihasilkan dalam format CSV memudahkan proses pelatihan dan pengujian model KNN. Melalui sistem ini, pelatih dan atlet dapat mendapatkan umpan balik yang terukur dan objektif tentang teknik servis, sehingga dapat meningkatkan latihan dan mencapai kemajuan kinerja yang signifikan dalam olahraga bulutangkis.
Keywords:
Bulu tangkis, K-Nearest Neighbors, Pendeteksian Gerakan, Penglihatan Komputer, Servis##plugins.themes.default.displayStats.downloads##
References
[1] B. He, “RETRACTED ARTICLE: Video teaching of piano playing and singing based on computer artificial intelligence system and virtual image processing,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. s1, p. 155, 2022, doi: 10.1007/s12652-021-03099-8.
[2] Y. Guo, J. Guo, C. Liu, H. Xiong, L. Chai, and D. He, “Precision landing test and simulation of the agricultural UAV on apron,” Sensors (Switzerland), vol. 20, no. 12, pp. 1–14, 2020, doi: 10.3390/s20123369.
[3] Z. Ding, Z. Feng, H. Li, D. Meng, Y. Zhang, and D. Li, “Experimental Study of Deep Submersible Structure Defect Monitoring Based on Flexible Interdigital Transducer Surface Acoustic Wave Technology,” Sensors, vol. 23, no. 3, 2023, doi: 10.3390/s23031184.
[4] H. Yu, Y. H. Son, H. J. Kim, K. Kim, P. S. Chang, and H. S. Jung, “Amperometric detection of conformational change of proteins using immobilized-liposome sensor system,” Sensors (Switzerland), vol. 18, no. 1, 2018, doi: 10.3390/s18010136.
[5] H. B. Zhang et al., “A comprehensive survey of vision-based human action recognition methods,” Sensors (Switzerland), vol. 19, no. 5, pp. 1–20, 2019, doi: 10.3390/s19051005.
[6] L. de Oliveira Carpes, L. B. Domingues, S. C. Fuchs, and R. Ferrari, “Rate of Responders for Post-Exercise Hypotension after Beach Tennis, Aerobic, Resistance and Combined Exercise Sessions in Adults with Hypertension,” Sports, vol. 11, no. 3, 2023, doi: 10.3390/sports11030058.
[7] Z. Wang, M. Guo, and C. Zhao, “Badminton Stroke Recognition Based on Body Sensor Networks,” IEEE Trans. Human-Machine Syst., vol. 46, no. 5, pp. 769–775, 2016, doi: 10.1109/THMS.2016.2571265.
[8] M. Vrigkas, C. Nikou, and I. A. Kakadiaris, “A review of human activity recognition methods,” Front. Robot. AI, vol. 2, no. NOV, pp. 1–28, 2015, doi: 10.3389/frobt.2015.00028.
[9] P. Ramya, R. S. Valarmathi, and C. Poongodi, “Retraction Note to: Antenna selection with improved group based particle swarm optimization (IGPSO) and joint adaptive beam forming for wideband millimeter wave communication (Journal of Ambient Intelligence and Humanized Computing, (2021), 12, 3, (4291-4,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. s1, p. 1, 2023, doi: 10.1007/s12652-022-03912-y.
[10] G. L. Goh, G. D. Goh, J. W. Pan, P. Soek, P. Teng, and P. W. Kong, “Automated Service Height Fault Detection Using Computer,” 2023.
[11] A. B. Rizki and E. Zuliarso, “Klasifikasi Teknik Bulutangkis Berdasarkan Pose Dengan Convulutional Neural Network,” J. Ilm. Inform., vol. 10, no. 02, pp. 96–101, 2022, doi: 10.33884/jif.v10i02.5559.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Legaspie Aura Sindhikara, Ardi Sanjaya, Ahmad Bagus Setiawan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- The author grants 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 acknowledgment 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 acknowledgment 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.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
