Detection of Indonesian Sign Language System using Convolutional Neural Network (CNN) with Nadam Optimizer

DOI

10.2991/978-94-6463-589-8_32How to use a DOI?

Keywords

Indonesian Sign Language; CNN; Nadam Optimizer; Deaf; Speech impaired

Abstract

This study enhances sign language detection by utilizing a Convolutional Neural Network (CNN) modified with the Nadam optimizer, resulting in improved accuracy in recognizing sign language images. The research begins by downloading the Indonesian Sign Language dataset from previous studies, which is then split into 80% for training and 20% for testing. Pre-processing is applied to ensure the dataset is compatible with the CNN algorithm. The dataset is then trained using a CNN model optimized with Nadam. After training, the model is tested, achieving an accuracy of 96.88% in detecting images from the Indonesian Sign Language system. These results show improved accuracy compared to previous studies. It is hoped that the findings of this study will benefit the deaf community, particularly in the learning process at special education schools in Indonesia.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Alamsyah Alamsyah
AU  - Dinda Ayu Anggraeni
PY  - 2024
DA  - 2024/12/01
TI  - Detection of Indonesian Sign Language System using Convolutional Neural Network (CNN) with Nadam Optimizer
BT  - Proceedings of the International Conference on Innovation & Entrepreneurship in Computing, Engineering & Science Education (InvENT 2024)
PB  - Atlantis Press
SP  - 352
EP  - 359
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-589-8_32
DO  - 10.2991/978-94-6463-589-8_32
ID  - Alamsyah2024
ER  -