Identification of Rupiah Banknote Authenticity for the Visually Impaired Using Convolutional Neural Network Architecture

This study investigates the efficacy of employing advanced Computer Vision (CV) techniques, particularly Convolutional Neural Networks (CNNs) such as EfficientNetV2 and VGG-19, to facilitate the detection of counterfeit money for visually impaired individuals. A mobile application leveraging these architectures was developed, enabling authentication through smartphone cameras without the need for specialized equipment. Rigorous evaluation, including dataset parameter variations and Grad-CAM analysis, was conducted to assess model performance. Results demonstrate the superiority of the EfficientNetV2-B2 architecture, achieving an accuracy of 94% and an F1-Macro score of 93%. VGG-19 also exhibited competitive performance with 85% accuracy and a 75% F1-Macro score. Furthermore, the implementation on Android devices demonstrated the application's ability, equipped with EfficientNetV2-B2, to accurately distinguish between genuine and counterfeit currency, achieving a confidence level of 99.93% for genuine banknotes and 97.67% for counterfeit ones. These findings highlight the potential of leveraging technology to enhance financial security and independence for visually impaired individuals without using LED and UV light assistance.