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All Journal International Journal of Robotics and Control Systems Bulletin of Social Informatics Theory and Application
Amaliya, Sholikhatul
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Social Sciences

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Worldwide mobile wallet: a futuristic cashless system Mumtaza, Qorina Mailil Husna; Nabillah, Shada Intishar; Amaliya, Sholikhatul; Rosabella, Yuveta; Hammad, Jehad Abdelhamid
Bulletin of Social Informatics Theory and Application Vol. 4 No. 2 (2020)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v4i2.204

Abstract

The proliferation of the internet led to the emergence of new technology. Nowadays, many activities can be done online, such a payment. After Mobile Banking was introduced publicly, a new method for payment called mobile wallet that uses mobile application as a device appears. The ease caused by this technology should be the main reason for implementing this system on to daily basis. Nevertheless, in various countries, the usage rate of the cashless system remains uneven, caused by many factors, both from internal factors such as security risks and external factors such as the policies of the countries involved. This paper will evaluate worldwide online payment systems to determine the impact of online payment systems usage, both positive and negative. This paper will evaluate the factors that affect the introduction of m-payment. Several measurements, such as country with the highest and lowest cashless payment usage and the reasons were taken for this research so that the objectives to improve the system and increase the usage rate can be achieved.
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.
Co-Authors Anik Nur Handayani Hammad, Jehad Abdelhamid Kurniawan, Wendy Cahya Liang, Yeoh Wen Muhammad Iqbal Akbar Mumtaza, Qorina Mailil Husna Nabillah, Shada Intishar Rosabella, Yuveta Wiryawan, Muhammad Zaki