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This Author published in this journals
All Journal CommIT (Communication & Information Technology) Indonesian Journal of Electrical Engineering and Computer Science
R. B. Wahyu
President University
Computer Science & IT

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Implementation of Real-Time Static Hand Gesture Recognition Using Artificial Neural Network Lita Yusnita; Rosalina Rosalina; Rusdianto Roestam; R. B. Wahyu
CommIT (Communication and Information Technology) Journal Vol. 11 No. 2 (2017): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v11i2.2282

Abstract

This paper implements static hand gesture recognition in recognizing the alphabetical sign from “A” to “Z”, number from “0” to “9”, and additional punctuation mark such as “Period”, “Question Mark”, and “Space” in Sistem Isyarat Bahasa Indonesia (SIBI). Hand gestures are obtained by evaluating the contourrepresentation from image segmentation of the glove wore by user. Then, it is classified using Artificial Neural Network (ANN) based on the training model previously built from 100 images for each gesture. The accuracy rate of hand gesture translation is calculated to be 90%. Moreover, speech translation recognizes NATO phonetic letter as the speech input for translation.
A transfer learning approach for real-time detection and classification of Indonesian coins Nur Hadisukmana; R. B. Wahyu; Stewart Qiu
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp856-864

Abstract

Automated currency recognition plays an important role in banking automation, retail systems, and assistive technologies. While banknote recognition has been extensively studied, coin recognition remains challenging due to small object size, metallic reflectance, visual similarity across denominations, and circulation-induced wear. This study proposes a real-time system for detecting and classifying Indonesian coins using a transfer learning–based deep learning approach. A curated dataset was developed to address the lack of publicly available training data for this domain. The model was initialized with pretrained weights and fine-tuned to adapt to the specific coin classification task. Experimental evaluation on an unseen test set demonstrates high detection accuracy while maintaining real time inference performance. Qualitative analysis under challenging conditions—including glare, low illumination, occlusion, and coin wear— reveals operational limitations and defines robustness boundaries. The findings confirm that frozen-backbone transfer learning provides an effective and computationally efficient strategy for adapting state-of-the-art object detectors to low-resource, domain-specific currency recognition tasks.
Co-Authors Lita Yusnita Nur Hadisukmana Rosalina Rosalina Rusdianto Roestam Stewart Qiu