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All Journal Media Jurnal Informatika
Akbar, Zulkaida
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Computer Science & IT

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Development Of A CNN Model For Recognizing The Indonesian Sign Language (BISINDO) Alphabet Nuriah, Sinta Siti; Sidiq, Ahmad Zamakhsyari; Akbar, Zulkaida
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5936

Abstract

Deaf people in Indonesia face communication barriers due to the limited understanding of Indonesian Sign Language (BISINDO) among the general public. This results in limited social interaction between deaf people and their surroundings. This study aims to develop a Deep Learning and Computer Vision-based BISINDO alphabet translator model using the Convolutional Neural Network (CNN) method, addressing the limited availability of publicly documented BISINDO datasets for alphabet recognition . The method used involves training the model with a dataset of 3,120 BISINDO alphabet images, covering the letters A to Z. The dataset was divided into 80% for training and 20% for testing. The training process included model architecture design, parameter tuning, selection of the best model based on accuracy, and performance evaluation. The evaluation results showed that the developed CNN model achieved an accuracy of 99.84% in classifying BISINDO letters; however, challenges remain in generalizing the model to variations in lighting, hand orientation, and user differences. Nevertheless, the high accuracy achieved indicates the model’s potential to support effective BISINDO translation and improve communication accessibility. This research also opens up opportunities for further development towards comprehensive translation of gestures or sentences in BISINDO.
Co-Authors Nuriah, Sinta Siti Sidiq, Ahmad Zamakhsyari