Real Time Indonesian Sign Language Hand Gesture Phonology Translation Using Deep Learning Model

Denny Pribadi; Mochamad Wahyudi; Diah Puspitasari; Agung Wibowo; Rizal Amegia Saputra; Rofi Saefurrohman

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Paper

Real Time Indonesian Sign Language Hand Gesture Phonology Translation Using Deep Learning Model

In Proceedings of the 3rd International Conference on Advanced Information Scientific Development ICAISD - Volume 1, 172-176, 2023 , Bekasi, Jawa Barat, Indonesia

Authors: Denny Pribadi ¹ ; Mochamad Wahyudi ² ; Diah Puspitasari ² ; Agung Wibowo ¹ ; Rizal Amegia Saputra ¹ and Rofi Saefurrohman ¹

Affiliations: ¹ Universitas Bina Sarana Informatika, Sukabumi, Indonesia ; ² Universitas Bina Sarana Informatika, Jakarta, Indonesia

Keyword(s): Real Time, Indonesia Sign Language, Hand Gesture Phonology, Deep Learning.

Abstract: In the era of Society 5.0, technology and computerization are almost applied to everything in this world. The advance- ment of computers is increasingly sophisticated, presenting so many software that helps a lot of human activities. Such as the image recognition feature that can be used to recognize and read sign language. The shape of the hand being sign language is a feature of phonology because the meaning of each sign can be distinguished according to the shape and gesture of the hand. SIBI (Indonesian Sign System) became the official language to be taught in extraordinary schools (SLB). In this study, the introduction of the A-Z alphabet as a SIBI sign language became the research material as the target language of translation applied to the application. The algorithms used are Deep Learning Convolutional Neural Network (CNN) and the Hand Gesture Recognition method, the training process in data processing experiments using 50 and 100 epoch experiments with a batch size of sixte en and a speed of 0.001 with a total of twenty-six classes. The resulting model is applied to build applications that can be used to detect and classify hand gestures on SIBI, resulting in outputs in the form of alphabetical and SIBI vocabulary. Researchers have previously conducted studies with a smaller number of classes. The results of the experiment on the application that has been built have a fast response time and have a higher accuracy rate than the earlier study, which was 85.3%. (More)

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Paper citation in several formats:

Pribadi, D.; Wahyudi, M.; Puspitasari, D.; Wibowo, A.; Amegia Saputra, R. and Saefurrohman, R. (2024). Real Time Indonesian Sign Language Hand Gesture Phonology Translation Using Deep Learning Model. In Proceedings of the 3rd International Conference on Advanced Information Scientific Development - ICAISD; ISBN 978-989-758-678-1, SciTePress, pages 172-176. DOI: 10.5220/0012446000003848

@conference{icaisd24,
author={Denny Pribadi. and Mochamad Wahyudi. and Diah Puspitasari. and Agung Wibowo. and Rizal {Amegia Saputra}. and Rofi Saefurrohman.},
title={Real Time Indonesian Sign Language Hand Gesture Phonology Translation Using Deep Learning Model},
booktitle={Proceedings of the 3rd International Conference on Advanced Information Scientific Development - ICAISD},
year={2024},
pages={172-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012446000003848},
isbn={978-989-758-678-1},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Information Scientific Development - ICAISD
TI - Real Time Indonesian Sign Language Hand Gesture Phonology Translation Using Deep Learning Model
SN - 978-989-758-678-1
AU - Pribadi, D.
AU - Wahyudi, M.
AU - Puspitasari, D.
AU - Wibowo, A.
AU - Amegia Saputra, R.
AU - Saefurrohman, R.
PY - 2024
SP - 172
EP - 176
DO - 10.5220/0012446000003848
PB - SciTePress