ABSTRACT
The household industry is the foundation of the existing home industry in Indonesia. It is categorized as a type of Small and Medium Enterprises (MSMEs) that significantly influence the Gross Domestic Product (GDP) ratio by up to approximately 10%. Since the pandemic of Covid-19, the home industry has increasingly stretched, especially in culinary and home craft products. The internet is one of the efforts to increase household industry sales. The household industry needs e-commerce to be a container for marketing its products. In this paper, we design an e-commerce system to support the sales productivity of the household industry in Indonesia. This study's e-commerce system or application is developed through some crucial stages. The stages are analysis through a questionnaire that represents needs in the field, selection of business models, namely B2B models with Virtual Storefront, marketplace concentrators, and lastly, Information Broker. Our infrastructure is determined for e-commerce development, and then strategy analysis is done using portfolio analysis, SWOT analysis, and competitor analysis. Based on the proposed strategy, we made a prototype as a designed e-commerce system that can increase sales for household businesses in cities in Indonesia. Important features in our proposed e-commerce system can accommodate many sellers or the household industry to establish relationships with many buyers based on geographical location, product search features based on closest positions or by city, and product categorization by familiar categories with household products. © 2023, Politeknik Negeri Padang. All rights reserved.
ABSTRACT
A surveillance system is still the most exciting and practical security system to prevent crime effectively. Surveillance systems run on edge devices such as the low-cost Raspberry mobile camera with the Internet of Things (IoT). The primary purpose of this system is to recognize the identity of the face caught by the camera. However, it raises the challenge of unstructured image/video where the video contains low quality, blur, and variations of human poses. Moreover, the challenge is increasing because people used to wear a mask during the Covid -19 pandemic. Therefore, we proposed developing an all-in-one surveillance system with face detection, recognition, and face tracking capabilities. The surveillance system integrated three modules: Multi-Task Cascaded Convolutional Network (MTCNN) face detector, VGGFace2 face recognition, and Discriminative Single-Shot Segmentation (D3S) tracker. We train new face mask data for face recognition and tracking. This system utilizes the Raspberry Pi camera and processes the frame on the cloud as a mobile sensor approach. The proposed method was successfully implemented and got competitive detection, recognition, and tracking results under an unconstrained surveillance camera. © 2021. All Rights Reserved.