Sistem Pendeteksi Tangan Berbasis Mediapipe Dan OpenCV Untuk Pengenalan Gerakan

Authors

  • Muhammad Arif Universitas Pamulang
  • Gemilang Saum Haryono Universitas Pamulang
  • Naufal Fawwazi Arsyad Universitas Pamulang
  • Rubby Ramadhani Airmas Sahid Universitas Pamulang
  • Perani Rosyani Universitas Pamulang

Keywords:

Pendeteksi Tangan, Mediapipe, OpenCV, Pengenalan Gerakan, Interaksi Manusia-Komputer

Abstract

This project aims to develop a real-time hand detection system using Mediapipe and OpenCV technology. This system is able to detect hands, identify hand landmark positions, and calculate numbers based on finger positions. This implementation makes important contributions to gesture recognition and human-computer interaction applications.

References

P. M. E. A. Rivan and A. Setiawan, "Pengenalan Gestur Angka Pada Tangan Menggunakan Arsitektur AlexNet Dan LeNet Pada Metode Convolutional Neural Network," Jurnal Sistem Komputer, vol. 11, pp. 19-28, 2022.

Priyonggo, A. Kusumah, A. Khumaidi, M. B. Rahmat, and J. Endrasmono, "Sistem Tracking Posisi Kamera Menggunakan Pengolahan Citra Untuk Pemusatan Posisi Pengambilan Video di Automation Academy," Jurnal Teknik Elektro dan Komputer TRIAC, vol. 9, 2022.

F. Damatraseta, R. Novariany, and M. A. Ridhani, "Real-time BISINDO Hand Gesture Detection and Recognition With Deep Learning CNN," Jurnal Informatikan Kesatuan (JIKES), vol. 1, 2021.

H. M. Putri, F. Fadlisyah, and W. Fuadi, "Pendeteksian Bahasa Isyarat Indonesia Secara Real-Time Menggunakan Long Short-Term Memory (LSTM)," Jurnal Teknologi Terapan and Sains 4.0, vol. 3, no. 1, pp. 13-25, 2022.

A. Halder and A. Tayade, "Real-time Vernacular Sign Language Recognitionusing MediaPipe and Machine Learning," International Journal of Research Publication and Reviews (IJRPR), vol. 2, no. 5, pp. 9-17, 2021.

F. Zhanget al., "MediaPipe Hands: On-Device Real-Time Hand Tracking," ArXiv, vol. abs/2006.10214, 2020.

R. Auziqni, “BISINDO INDONESIAN SIGN LANGUAGE RECOGNITION USING MEDIAPIPE HOLISTIC AND LSTM DEEP LEARNING MODEL Thesis Report.” [Online]. Available: http://digilib.mercubuana.ac.id/

S. N. Budiman, S. Lestanti, S. M. Evvandri, and R. K. Putri, ‘PENGENALAN GESTUR GERAKAN JARI UNTUK MENGONTROL VOLUME DI KOMPUTER MENGGUNAKAN LIBRARY OPENCV DAN MEDIAPIPE’, Antivirus : Jurnal Ilmiah Teknik Informatika, vol. 16, no. 2, Art. no. 2, Nov. 2022, doi: 10.35457/antivirus.v16i2.2508.

M. Maryamah, M. A. Pratama, M. R. Erfit, N. M. Farhani, and I. A. Hartono, ‘Klasifikasi Abjad SIBI (Sistem Bahasa Isyarat Indonesia) menggunakan Mediapipe dengan Metode Deep Learning’, PROSIDING SEMINAR NASIONAL SAINS DATA, vol. 3, no. 1, Art. no. 1, Nov. 2023, doi: 10.33005/senada.v3i1.102.

Published

2024-07-18

How to Cite

Arif, M. ., Saum Haryono, G. ., Fawwazi Arsyad, N. ., Ramadhani Airmas Sahid, R. ., & Rosyani , P. . (2024). Sistem Pendeteksi Tangan Berbasis Mediapipe Dan OpenCV Untuk Pengenalan Gerakan. BINER : Jurnal Ilmu Komputer, Teknik Dan Multimedia, 2(2), 173–177. Retrieved from https://journal.mediapublikasi.id/index.php/Biner/article/view/4323