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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Jurnal Masharif al-Syariah: Jurnal Ekonomi dan Perbankan Syariah Indonesian Journal of Artificial Intelligence and Data Mining JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Kumparan Fisika Jurnal Teknologi Dan Sistem Informasi Bisnis Jambura Journal of Electrical and Electronics Engineering Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Pengabdian kepada Masyarakat Nusantara Action Research Literate (ARL) Jurnal Sisfotek Global International Journal Science and Technology (IJST) Journal of Scientech Research and Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Journal of Community Service
M. Asep Rizkiawan
Politeknik Takumi
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Other

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Journal : Jurnal Informatika

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Pose Analysis and Classification in Shooting Sport Using Convolutional Neural Network and Long Short-Term Memory Sobari, Bahar; Moedjiono, Moedjiono; Rizkiawan, M. Asep
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25566

Abstract

Shooting sport requires high accuracy and speed, making training evaluation essential for athlete performance improvement. Conventional evaluation methods are often limited, thus the application of Artificial Intelligence (AI) and Computer Vision provides an effective alternative. This research aims to analyze and classify shooting sport poses using Deep Learning methods. A dataset consisting of several thousand pose images was collected from both field recordings and publicly available sources, followed by preprocessing for coordinate extraction. Convolutional Neural Network (CNN) was employed to extract coordinate data from shooting pose images, while Long Short-Term Memory (LSTM) was applied for pose classification. Experimental results demonstrated 94% accuracy, 95% Percentage of Correct Keypoints (PCK), and 4 mm Mean Per Joint Position Error (MPJPE), with training conducted at a learning rate of 0.0001 over 150 epochs on 5% test data, involving a total of 596,642 parameters. These results indicate that the proposed CNN–LSTM model provides a reliable approach for pose analysis and classification in shooting sport. The contribution of this study lies in presenting a novel dataset and framework for AI-based shooting sport evaluation, which can enhance training feedback and broaden AI applications in sports. 
Co-Authors Abdul Multi Agus Sofwan Sofwan, Agus Sofwan Aji, Arielio Bayu Ajitomo, Dimas Suryo Alim, Endi Sjaiful Alim, Endy Sjaiful Anhari, Tirta Ardiansyah , Bintang Aulia, Muhammad Fathan Avista, Zeluyvenca Eko Kurniawan Eko Kurniawan Emilia Roza Endy Sjaiful Alim FADLY, SINGGI Firman Noor Hasan Gaol , Ford Lumban Hadi, Nofiar Harry Ramza Harry Ramza Hoirul Anam Kurniawan, Eko Kurniawan Matsuo , Takuro Moedjiono Moedjiono Nuroji Nuroji Nuroji Nuroji Prananda, Dimas Adi Rahmi Imanda Ramza, Harry Rosalina Singgi Fadly Sobari, Bahar Sofwan, Agus subagyo, Ahmad Sulaeman, Mia Kamayani Wijaya, Farid Rizky Yudha Witanto, Yudha Zeluyvenca Avista