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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Andaru, Gabriel Imam
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Computer Science & IT Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Pengembangan Sistem Deteksi On-Shelf Availability Produk Menggunakan Algoritma YOLOV8 pada Aplikasi Beregerak Andaru, Gabriel Imam; Fudholi, Dhomas Hatta
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 2 (2024): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i2.767

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.
YOLO-based Small-scaled Model for On-Shelf Availability in Retail Fudholi, Dhomas Hatta; Kurniawardhani, Arrie; Andaru, Gabriel Imam; Alhanafi, Ahmad Azzam; Najmudin, Nabil
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5600

Abstract

The availability of the shelf (OSA) in the retail industry plays a very crucial role in continuous sales. Unavailability of products can make a bad impression on customers and reduce sales. The retail industry may continue to develop through the rapidly advancing technology era to thrive in a market where competition is increasingly tough. Along with technological advances in recent decades, artificial intelligence has begun to be applied to support OSA, particularly by using object detection technology. In this research, we develop a small-scale object detection model based on four versions of the You Only Look Once (YOLO) algorithm, namely YOLOv5-nano, YOLOv6-nano, YOLOv7-tiny, and YOLOv8-nano. The developed model can be used to support automatic detection of OSA. A small-scale model has developed in the sense of postpractical implementation through low-cost mobile applications. We also use the quantization method to reduce the model size, INT8 and FP16. This small-scale model implementation also offers flexibility in implementation. With a total of 7697 milk-based retail product images and 125 different product classes, the experiment results show that the developed YOLOv8-nano model, with a mAP50 score of 0.933 and an inference time of 13.4 ms, achieved the best performance.
Co-Authors Alhanafi, Ahmad Azzam Arrie Kurniawardhani Fudholi, Dhomas Hatta Najmudin, Nabil