Development of Smart Money Classifier & Location Tracker Application Based on Convolutional Neural Network (CNN) for The Visually Impaired - Mustafa, Lis Diana; Dali, Sri Wahyuni; -Wahidah, Khoirunnisa (JURNAL JARTEL: Jurnal Jaringan Telekomunikasi, 2026)
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.