Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization International Journal of Robotics and Control Systems
Liang, Yeoh Wen
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Hand Keypoint-Based CNN for SIBI Sign Language Recognition Handayani, Anik Nur; Amaliya, Sholikhatul; Akbar, Muhammad Iqbal; Wiryawan, Muhammad Zaki; Liang, Yeoh Wen; Kurniawan, Wendy Cahya
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1745

Abstract

SIBI is less widely adopted, and the lack of an efficient recognition system limits its accessibility. SIBI gestures often involve subtle hand movements and complex finger configurations, requiring precise feature extraction and classification techniques. This study addresses these issues using a Hand Keypoint-based Convolutional Neural Network (HK-CNN) for SIBI classification. The research utilizes Kinect 2.0 for precise data collection, enabling accurate hand keypoint detection and preprocessing. The optimal data acquisition distance between 50 and 60 cm from the camera is considered to obtain clear and detailed images. The methodology includes four key stages: data collection, preprocessing (keypoint extraction and image filtering), classification using HK-CNN with ResNet-50, EfficientNet, and InceptionV3, and performance evaluation. Experimental results demonstrate that EfficientNet achieves the highest accuracy of 99.1% in the 60:40 data split scenario, with superior precision and recall, making it ideal for real-time applications. ResNet-50 also performs well with 99.3% accuracy in the 20:80 split but requires longer computation time, while InceptionV3 is less efficient for real-time applications. Compared to traditional CNN methods, HK-CNN significantly enhances accuracy and efficiency. In conclusion, this study provides a robust and adaptable solution for SIBI recognition, facilitating inclusivity in education, public services, and workplace communication. Future research should expand dataset diversity and explore dynamic gesture recognition for further improvements.
Mono Background and Multi Background Datasets Comparison Study for Indonesian Sign Language (SIBI) Letters Detection using YOLOv8 Andriyanto, Teguh; Handayani, Anik Nur; Ar Rosyid, Harits; Wiryawan, Muhammad Zaki; Azizah, Desi Fatkhi; Liang, Yeoh Wen
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3462

Abstract

The research in this paper focuses on the detection of Indonesian Sign Language System (SIBI) letters using the YOLOv8 object detection model. The study compares two datasets, one with mono-background (a simple, uniform background) and another with multi-background (complex and varied backgrounds). The research aims to evaluate how the complexity of image backgrounds affects the performance of the YOLOv8 model in detecting SIBI letters This study uses a dataset consisting of 24 SIBI letters (excluding J and Z due to the complexity of their gestures), sourced from Mendeley. The dataset was processed with and without data augmentation (rotation, brightness adjustments, blur, and noise) to test the model under various conditions. Four models were trained and tested: one using mono-background images, another using augmented mono-background images, a third using multi-background images, and a final model trained on augmented multi-background images. The results showed that the YOLOv8 model performed best with the multi-background dataset, achieving a precision of 0.995, recall of 1.000, F1 score of 0.997, and mAP50 of 0.994Adding to the model made it better at generalizing, but it took longer to train. The study finds that multi-background datasets with augmentation make the model much better at finding SIBI letters in real-world settings. This makes it a promising tool for projects that aim to improve communication for deaf people in Indonesia. The study suggests that more research should be done on augmentation techniques and bigger datasets to make detection more accurate. 
Co-Authors Amaliya, Sholikhatul Anik Nur Handayani Azizah, Desi Fatkhi Harits Ar Rosyid Kurniawan, Wendy Cahya Muhammad Iqbal Akbar Teguh Andriyanto, Teguh Wiryawan, Muhammad Zaki