Introduction of Indonesian Significant Alphabet Images (BISINDO) using The Convolutional Neural Network Algorithm

Bisindo alphabet recognition is the process by which a computer system or software recognizes and recognizes the letters of the Bisindo alphabet. The Bisindo alphabet is a special alphabet used to communicate with people who are hearing or speech impaired. This process uses image processing and machine learning techniques to identify and classify each letter based on its shape and visual characteristics. This study used a dataset consisting of 520 Kaggle images divided into 26 categories. These images are resized, normalized and scaled up to improve model performance. A Convolutional Neural Network (CNN) model was developed and achieved 99.12587% accuracy after training. After the model was developed, the API was implemented using Flask. API functionality is tested using online interactions, ensuring accurate responses to image classification before implementation in mobile applications.

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Publication Details

Journal
JURNAL SISFOTEK GLOBAL
Published
2025-03-31
DOI
https://doi.org/10.38101/sisfotek.v15i1.15678
Primary Topic
Computer Science and Engineering
Type
article
Field-Weighted Citation Impact
0.00
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article

Introduction of Indonesian Significant Alphabet Images (BISINDO) using The Convolutional Neural Network Algorithm

Debora Chrisinta, Budiman Baso, Yoseph Pius Kurniawan Kelen, Stefanus Kabut
JURNAL SISFOTEK GLOBAL
Computer Science and Engineering
article

Introduction of Indonesian Significant Alphabet Images (BISINDO) using The Convolutional Neural Network Algorithm

Debora Chrisinta, Budiman Baso, Yoseph Pius Kurniawan Kelen, Stefanus Kabut
article en

Abstract

Bisindo alphabet recognition is the process by which a computer system or software recognizes and recognizes the letters of the Bisindo alphabet. The Bisindo alphabet is a special alphabet used to communicate with people who are hearing or speech impaired. This process uses image processing and machine learning techniques to identify and classify each letter based on its shape and visual characteristics. This study used a dataset consisting of 520 Kaggle images divided into 26 categories. These images are resized, normalized and scaled up to improve model performance. A Convolutional Neural Network (CNN) model was developed and achieved 99.12587% accuracy after training. After the model was developed, the API was implemented using Flask. API functionality is tested using online interactions, ensuring accurate responses to image classification before implementation in mobile applications.

JURNAL SISFOTEK GLOBALVol. 15(1)
University of Timor (ID)
Openalex Percentile: Top 97%
Computer Science and Engineering
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Introduction of Indonesian Significant Alphabet Images (BISINDO) using The Convolutional Neural Network Algorithm — Debora Chrisinta, Budiman Baso, et al. · JURNAL SISFOTEK GLOBAL (2025) | TGRS Research Map | TGRS