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JUTEI (Jurnal Terapan Teknologi Informasi)
Published by Universitas Kristen Duta Wacana
ISSN : 25793675     EISSN : 25795538     DOI : https://doi.org/10.21460/jutei
Core Subject : Science,
Computer Science & IT
Jurnal Terapan Teknologi Informasi (JUTEI) is a journal focusing on theory, practice, and methodology of all aspects in Information Technology and Computer Science, as well as productive and innovative ideas related to new technology and applied sciences. This journal is managed by the Faculty of Information Technology, Duta Wacana Christian University. The subject of articles may cover, but is not limited to: -Artificial Intelligence -Business Intelligence -Cloud & Grid Computing -Computer Networking & Security -Computer Vision -Datawarehouse & Datamining -Decision Support System -Digital Signal Processing -E-System -Enterprise Architecture -Enterprise System (SCM, ERP, CRM) -Human Computer & Interaction -Image Processing -Information Retrieval -Information System -Information Systems Audit -Internet of Things -Knowledge Management -Mobile Computing & Application -Multimedia System -Open Source System & Technology -Semantic Web -Software Engineering -User Interface/ User Experience (UI/UX)
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 111 Documents
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Pengenalan Gerakan Olahraga (Push Up dan Sit Up) menggunakan Mediapipe Hendisutio, Andrew; Alfarabi, Azis Dzaffin; Wahab, Wahidin; Wulandari, Meirista
Jurnal Terapan Teknologi Informasi Vol 10 No 1 (2026): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2026.101.477

Abstract

Motion in sports workouts plays a crucial role in evaluation and learning. This design aims to develop a sports workout movement recognition device based on image processing with high accuracy. The design methodology involves acquiring image data of workout movements and training a recognition model using Mediapipe, which is a deep learning-based object detection algorithm. Image data of workout movements is obtained from push-up and sit-up exercises performed in front of the Sports Movement Acquisition module. The training process of the Mediapipe model utilizes this input data to recognize movements such as push-ups and sit-ups. Initial experimental results indicate that the system can recognize workout movements with satisfactory accuracy. Furthermore, this research includes testing the system with varying distances to determine its accuracy at different ranges. In further experiments, the system can be refined or improved to achieve higher accuracy. This research contributes significantly to the development of sports workout movement recognition technology using the Mediapipe algorithm, focusing on the classification of push-up and sit-up movements. The system can be used in training, evaluation, and assessment of specific workout movements, namely push-ups and sit-ups. The integration of image processing and deep learning in this field holds potential for further development in movement analysis and training for both athletes and beginners. By combining modern technology and sports science, this research opens new opportunities for understanding and enhancing workout movements, particularly in push-up and sit-up exercises, potentially providing significant benefits in athlete training and the development of workout sports.

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