Muslikhin, Y. Indrihapsari, A.A. Baiti, M.S. Wang
The penetration of Industry 4.0 in the convenience store is increasingly visible. The combination of Artificial Intelligence (AI), the Internet of Things (IoT), and robotic were always under evaluation, including applied AI in the whole system that is demanded robust when integrated with online shop. Meanwhile, the problem of speed detection and precision is still a challenge in AI. In this study data-driven deep learning was developed to improve the speed and maximize localization of consumer products. After the selected data-driven received from the online shop platform then triggers deep learning and activates a modified YOLOv2 in parallel. Thus, each detector per product is needed. Our system performance is proven by experiments to meet expectations in evaluating speed, precision, and performance in recognition and localization. © 2021 Published under licence by IOP Publishing Ltd.
Department of Electronics Engineering Education, Universitas Negeri Yogyakarta, Yogyakarta City, 55281, Indonesia; Department of Electrical Engineering, Southern Taiwan University of Science Technology, Tainan City, 710, Taiwan; Department of Information Technology, Universitas Negeri Yogyakarta, Yogyakarta City, 55281, Indonesia; Department of Informatics Management, National Taiwan University of Science Technology, Taipei City, 106, Taiwan
