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All Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Suci Aulia
School of Applied Science, Telkom University, Bandung, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Identifikasi Atlet Anggar Berdasarkan Pengukuran Antropometri Menggunakan MediaPipe Pose Bagas Alif Fimaskoro; Suci Aulia; Dery Rimasa
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 1: Februari 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i1.8145

Abstract

Over time, numerous developments in digital technology have benefited people, including anthropometric measurements that provide information on an athlete’s ability in sports. The use of digital technology in sports must continue, particularly in the National Sports Committee of Indonesia (Komite Olahraga Nasional Indonesia, KONI) of Bandung City. This study proposed a technique for classifying and identifying fencing athletes’ talents. This work developed a methodology for evaluating sports talent based on anthropometric measurements of athletes’ bodies using the posture detection approach. Fencing and nonfencing athletes in KONI Bandung City were categorized using this talent identification. This study used 36 datasets of body posture images from various skills of the sport. These images were in JPEG or JPG format with a resolution of 3,024 × 4,032 and were acquired using a Canon EOS 1300D camera. This study utilized four points landmarks, which are usually used as measurement components in KONI, to categorize fencing athletes. The four points are shoulder (S), elbow (E), index (I), and hip (H) landmarks. The testing was done using three different dataset settings. According to the test results of all scenarios, scenario 2 had the highest accuracy. This scenario was able to categorize fencing and nonfencing athletes with an accuracy rate of 89% and an average processing time of less than 3 s per image.
Co-Authors Bagas Alif Fimaskoro Rimasa, Dery