Deep Learning Based Technical Classification of Badminton Pose with Convolutional Neural Networks

Tukino Tukino(1*); Mutiana Pratiwi(2); Sarjon Defit(3);

(1) Universitas Putera Batam
(2) Universitas Putra Indonesia YPTK Padang
(3) Universitas Putra Indonesia YPTK Padang
(*) Corresponding Author



This research aims to identify and categorize badminton strategies using a Convolutional Neural Network (CNN) model combined with BlazePose architecture and Mediapipe Pose Solution tools, yielding understandable and practical results. The challenge of finding the best mobility strategy for badminton serves as the primary motivation for this study. The research employs an image recognition and supervised learning approach to classify poses in badminton training videos. The training data comprises various photos and images representing different badminton techniques, such as Service Technique and Smash Technique. After data processing, the CNN model is trained using the training data to identify and classify poses in badminton training videos. Testing is conducted using test data, and classification accuracy is evaluated using the CNN method. The results show that the CNN model implemented alongside BlazePose and Mediapipe Pose Solution achieves significant classification accuracy, ranging from 80% to 90%. Thus, this research presents an effective and practical method for classifying badminton strategies based on poses in training videos.


Badminton; Classification; Convolutional Neural Networks; Deep Learning; Estimation Pose


Full Text:


Article Metrics

Abstract view: 94 times
PDF view: 35 times

Digital Object Identifier




Hendriyana and Y. H. Maulana, "Identifikasi Jenis Kayu menggunakan Convolutional Neural Network dengan Arsitektur Mobilenet," J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 1, pp. 70–76, 2020, doi: 10.29207/resti.v4i1.1445.

F. Endrianti, W. Setiawan, and Y. Wihardi, "Sistem Pencatatan Kehadiran Otomatis di Ruang Kelas Berbasis Pengenalan Wajah Menggunakan Metode Convolutional Neural Network (CNN)," JATIKOM - J. Apl. dan Teori. Ilmu Komput., vol. 1, no. 1, pp. 40–44, 2018, doi: 10.17509/jatikom.v1i1.25146.

H. S. Ibrahim, M. Si, U. N. Wisesty, F. Informatika, and U. Telkom, "Analisis Deep Learning Untuk Mengenali Qrs Kompleks Pada Sinyal Ecg Dengan Metode CNN," e-Proceeding Eng., vol. 5, no. 2, pp. 3718–3725, 2018.

M. Zufar and B. Setiyono, "Convolutional Neural Networks Untuk Pengenalan Wajah Secara Real-Time," J. Sains dan Seni ITS, vol. 5, no. 2, p. 128862, 2021.

F. Han, J. Yao, H. Zhu, and C. Wang, "Underwater Image Processing and Object Detection Based on Deep CNN Method," J. Sensors, vol. 2020, p. 20, 2020, doi: 10.1155/2020/6707328.

M. Elleuch, R. Maalej, and M. Kherallah, "A New design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition," Procedia Comput. Sci., vol. 80, pp. 1712–1723, 2016, doi: 10.1016/j.procs.2016.05.512.

B. Sekeroglu and I. Ozsahin, "Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks," SLAS Technol., vol. 25, no. 6, pp. 553–565, 2020, doi: 10.1177/2472630320958376.

P. Ma, "Recognition of handwritten digit using convolutional neural network," Proc. - 2020 Int. Conf. Comput. Data Sci. CDS 2020, vol. 19, no. 2, pp. 183–190, 2020.

S. Indolia, A. K. Goswami, S. P. Mishra, and P. Asopa, "Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach," Procedia Comput. Sci., vol. 132, pp. 679–688, 2018, doi: 10.1016/j.procs.2018.05.069.

P. Wang, X. Zhang, and Y. Hao, "A Method Combining CNN and ELM for Feature Extraction and Classification of SAR Image," J. Sensors, vol. 2019, doi: 10.1155/2019/6134610.

A. Patil and M. Rane, "Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition," Smart Innov. Syst. Technol., vol. 195, pp. 21–30, 2021, doi: 10.1007/978-981-15-7078-0_3.

W. Lee, K. Ko, Z. Geem, K. Sim, and E. Engineering, "Method that Determining the Hyperparameter of CNN using HS algorithm," J. Korean Inst. Intell. Syst., vol. 27, no. 1, pp. 22–28, 2017.

M. Sadeghi et al., "PERSIANN-CNN: Precipitation estimation from remotely sensed information using artificial neural networks–convolutional neural networks," J. Hydrometeorol., vol. 20, no. 12, pp. 2273–2289, 2019, doi: 10.1175/JHM-D-19-0110.1.

Y. Supardi and D. Dede, Semua Bisa Menjadi Programmer Python Case Study. Jakarta: Elex Media Komputindo, 2020.

A. R. Syulistyo, D. M. J. Purnomo, M. F. Rachmadi, and A.Wibowo, "Convolutions Subsampling Convolutions Gaussian connection Full connection Full connection Subsampling," JIKI (Jurnal Ilmu Komput. dan Informasi) UI, vol. 9, no. 1, pp. 52–58, 2020.

P. A. Nugroho, I. Fenriana, and R. Arijanto, "Implementasi Deep Learning Menggunakan Convolutional Neural Network (CNN) Pada Ekspresi Manusia," Algor, vol. 2, pp. 12–21, 2020.

P. Lakhani, B. Sundaram, “Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks”. Radiology 284:574–582, 2021, doi: 10.1148/radiol.2017162326.

F. Lu, F. Wu, P. Hu, Z. Peng, D. Kong D, "Automatic 3D liver location and segmentation via convolutional neural network and graph cut". Int J Comput Assist Radiol Surg 12:171–182, 2022, doi: 10.1007/s11548-016-1467-3.

F. Milletari, N. Navab, S-A. Ahmadi, "V-net: fully convolutional neural networks for volumetric medical image segmentation". In: Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV) , 2021, doi: 10.1109/3DV. 2016.79.

PF. Christ, MEA. Elshaer, F. Ettlinger et al, "Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields". In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, Wells W (eds) Proceedings of Medical image computing and computer-assisted intervention –MICCAI 2021, doi: 10.1007/978-3-319-46723-8_48.

Shafira, Tiara. (2018). Implementasi Convolutional Neural Networks Untuk Klasifikasi Citra Tomat Menggunakan Keras. Skripsi. Fakultas Matematika dan Ilmu Pengetahuan Alam. Universitas Islam Indonesia : Yogyakarta.

Dewa C. K., Fadhilah A. L. and Afiahayati. (2018). Convolutional Neural Networks for Handwritten Javanese Character Recognition IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 93-94, doi: 10.22146/ijccs.31144.

F. Han, J. Yao, H. Zhu, and C. Wang, “Underwater Image Processing and Object Detection Based on Deep CNN Method,” J. Sensors, vol. 2020, p. 20, 2020, doi: 10.1155/2020/6707328.


Copyright (c) 2024 Tukino Tukino, Mutiana Pratiwi, Sarjon Defit

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.