Implementasi YOLOv11 dan Google ML Kit untuk Pembacaan Struk pada Aplikasi Keuangan Mobile

Zufar, Muhammad Viddya
Pratiwi, Nunik

METIK JURNAL
Vol. 9 No. 2 (2025): METIK Jurnal

This study aims to develop an Android-based system capable of automatically recapping shopping data from cashier receipts. The system integrates the YOLOv11 object detection method to identify key information areas such as product names, quantity, unit price, and total amount, and utilizes Google ML Kit as the Optical Character Recognition (OCR) module to extract text from receipt images. The research stages include problem identification, system design, prototype development, and performance evaluation using the Confusion Matrix method. The testing results show a precision of 100%, recall of 74.96%, and an F1-score of 85.7%, indicating that the system performs with high accuracy and effectiveness in extracting receipt information. Therefore, this system offers a practical and efficient solution for automatic expense recording through mobile devices.