Implementasi CNN dan MediaPipe dalam Peningkatan Efektivitas Stretching pada Olahraga Futsal: Implementation of CNN and MediaPipe in Increasing the Effectiveness of Stretching in Futsal Sports

Vito Jericho; Theresia Herlina Rochadiani

doi:10.33050/tmj.v9i3.2294

Authors

Vito Jericho Pradita University
Theresia Herlina Rochadiani Pradita University

DOI:

https://doi.org/10.33050/tmj.v9i3.2294

Abstract

This study aims to develop an effective Convolutional Neural Network (CNN) model in recognizing stretching
movements that are often performed by futsal players, with the aim of reducing the risk of injury. The dataset used
consists of 3000 images covering five types of movements: High Knees, Jumping Jacks, Lunge, Side Lunge, and Butt
Kicks. The data was taken from YouTube videos and processed to produce landmarks through MediaPipe technology. The CNN model was trained using the ”Adam” optimizer, with 50 epochs, a batch size of 8, and a learning rate of 0.001. The training results showed an accuracy of 94%, with the best performance on the Lunge and Jumping Jack movements, and adequate performance on other movements. The implementation of this model allows real-time monitoring of stretching movements, provides direct feedback to users, and helps futsal players in stretching with the right technique to avoid injury. This study shows that the CNN-based approach for stretching motion recognition in futsal is effective and reliable. Further
research is suggested to increase the amount of training data and explore different model architectures to strengthen the model’s generalization.

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