MONITORING AND DETECTOR PHYSICAL DISTANCE FOR COVID�19 USE DEEP LEARNING WITH ALGORITHM YOLOv4

Nai’inin, Ferrys (2022) MONITORING AND DETECTOR PHYSICAL DISTANCE FOR COVID�19 USE DEEP LEARNING WITH ALGORITHM YOLOv4. Diploma thesis, Politeknik Negeri Jember.

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Abstract

Corona Virus Disease 2019 (Covid-19) as an outbreak or pandemic on March 14, 2020 announced by the Head of the Disaster Management Agency. People infected with the Covid-19 virus are characterized to experience flu symptoms accompanied by fever, runny nose, dry cough, sore throat, and headache (Yuliana, 2020). By designating it as a disaster, the government imposes the community to comply with health regulations, namely wearing masks, maintaining distance, and washing hands with running water, and this has caused changes in the lifestyle of the Indonesian people so that many people are not used to the policy, this is proven that there are still many violations committed by several communities. In this study, the YOLOv4 Algorithm used an artificial neural network (JST) approach to detect objects in an image. The result of this study is a python-based application to detect crowds or physical distancing. The result of this study is a python-based application to detect crowds or physical distancing. The detection carried out has an accuracy value of 75% by conducting 3000 experiments

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