JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi)
Vol. 12 No. 1 (2024): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI

Integrasi Metode Convolutional Neural Networks dengan Arsitektur Model PoseNet untuk Pengembangan Sistem Klasifikasi Gerakan serta Monitoring Repetisi pada Olahraga Bulu Tangkis

Benny Karnadi (Unknown)
Chairisni Lubis (Unknown)
Agus Budi Dharmawan (Unknown)



Article Info

Publish Date
20 Jan 2024

Abstract

This application or a recognition of movement classification system and monitoring repetitions in badminton system is designed for students and coaches to practice movement and stroke techniques in badminton as well as a joint evaluation medium for users who practice using this application, also with the hope that it can help children who want to practice Badminton can be more flexible in terms of time and also efficient for coaches to be able to reach a wider range of students and produce more talented athletes. One of the branches of science used in designing this application is Deep Learning with the Convolutional Neural Network (CNN) method with the MobileNetv2 architecture used in designing this badminton movement classification application, as well as using PoseNet model integration. The training results that can be achieved using the Convolutional Neural Network method with the MobileNetv2 architecture obtain an accuracy score in the range of 90%, and test results can be achieved with an accuracy score of 93%.

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Journal Info

Abbrev

jiksi

Publisher

Subject

Computer Science & IT Mathematics Other

Description

Jurnal Ilmu Komputer dan Sistem Informasi (JIKSI) diterbitkan oleh Fakultas Teknologi Informasi Universitas Tarumanagara (FTI Untar) Jakarta sebagai media publikasi karya ilmiah mahasiswa program studi Teknik Informatika dan Sistem Informasi FTI Untar. Karya-karya ilmiah yang dihasilkan berupa hasil ...