Pengembangan Sistem Deteksi On-Shelf Availability Produk Menggunakan Algoritma YOLOV8 pada Aplikasi Beregerak

Abstract

Research related to retail operations has been a major focus in recent years, driven by rapidly changing market dynamics and the importance of product availability on store shelves to meet customer satisfaction. The concept of On Shelf Availability (OSA) has become key in ensuring products are available when needed. However, the challenge in retail management lies in monitoring thousands of different products, which is time-consuming and resource-intensive. To address this issue, an efficient object detection solution is needed. The research implements the YOLOv8 algorithm in detecting out-of-stock items, particularly in the context of mobile devices with resource limitations. In order to achieve this goal, the research adopts a comprehensive methodology, starting from direct data collection from supermarkets, data processing, labeling, to model training using transfer learning techniques. Transfer learning method is chosen to overcome data limitations and accelerate the model training process, enabling faster adaptation to object detection conditions at specific locations. Test results show that YOLOv8s delivers the best performance with an accuracy of up to 94.7%, allowing real-time object detection. Testing is conducted on various mobile devices, including Samsung A54 and Samsung A6, where YOLOv8n consistently performs with an inference time of 41.46 ms on Samsung A54 and 257.73 ms on Samsung A6. The main contribution of this research is to enhance object detection capabilities on devices with low computational power, such as mobile devices, and provide an effective solution to the problem of product availability on store shelves. Thus, this research not only brings positive impact to retail management but also drives the development of object detection technology in the context of resource-limited devices and unstable internet connections.