Development of Smart Money Classifier & Location Tracker Application Based on Convolutional Neural Network (CNN) for The Visually Impaired
Year: 2026
Type: article
Abstract: The number of visually impaired individuals in Indonesia continues to grow each year, caused by both congenital factors and age-related conditions. This situation positions Indonesia as the country with the second-highest number of blind people globally after Ethiopia. To address this, the present research introduces an Android-based mobile application named Smart Money Classifier & Location Tracker, specifically designed to assist visually impaired individuals in independently recognizing Indonesian banknote denominations and tracking their real-time location. The system integrates Convolutional Neural Network (CNN) technology for banknote image classification using a smartphone camera, enhanced with text-to-speech to announce recognition results. Moreover, a GPS-based tracking feature enables family members to monitor the user’s position through a dedicated interface that records chronological location history. Testing involved worn and folded banknotes under varied lighting and distances. Results indicate recognition accuracy of 85–99% under optimal conditions. For location tracking, the system estimated positions with average accuracy of 5–15 meters in open areas and 15–25 meters in semi-enclosed spaces, with a data transmission delay of 2–4 seconds. By combining these features, the application improves independence, safety, and convenience for visually impaired individuals, supporting them in everyday life.
Institution: Politeknik Negeri Malang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $19
OpenAlex ID: W7147200736
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