PENGEMBANGAN SISTEM KASIR PINTAR BERBASIS WEB DENGAN ON DEVICE OBJECT DETECTION MENGGUNAKAN YOLOV8-NANO UNTUK IDENTIFIKASI PRODUK OTOMATIS PADA UMKM ANEKA KUE PAK YANTO

Adnan Wahab; Dadang Iskandar Mulyana; Mesra Betty Yel

doi:10.31539/jvfyj344

Authors

Adnan Wahab Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
Dadang Iskandar Mulyana Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
Mesra Betty Yel Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

DOI:

https://doi.org/10.31539/jvfyj344

Abstract

Transaction activities at Aneka Kue Pak Yanto MSME are still performed manually, leading to recording errors, slower service, and difficulties in generating sales reports. This study aims to develop a web-based smart cashier system by integrating on-device object detection using YOLOv8-Nano to automatically identify 21 bakery products without barcode scanning. The system was developed using the Waterfall method, with Laravel as the backend framework and ONNX Runtime Web to perform local inference directly within the web browser. The dataset was created through image acquisition, annotation, and data augmentation before training the detection model. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and Mean Average Precision (mAP). The proposed system is expected to accelerate transaction processing, minimize human errors, improve product identification accuracy, and support the digital transformation of MSMEs through an efficient, intelligent, and browser-based cashier solution.

Keywords : Smart Cashier System, YOLOv8-Nano, On-Device Object Detection, ONNX Runtime Web, MSMEs.

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