ABSTRACT
COVID-19 can be spread through the air, caused by inhaling smaller droplets1 containing SARS-CoV-2 in an indoor environment. In particular, both people with symptoms and people without symptoms make many small droplets and respiratory droplets when they breathe, sneeze, cough, or speak. When these tiny droplets are exposed to the air around them, they can react with the particles (PM) and stay in the air for a long time. The survival period depends on various conditions, including the type of surface, temperature, and relative humidity. The ventilation system is one way to solve this problem. This research makes a significant contribution to the development of intelligent ventilation systems utilizing the Internet of Things (IoT) and decision tree algorithms. The function of this system is to get a clean room environment from viruses by spraying disinfectant automatically and controlling the temperature and humidity of the air. Based on the results of this study, the system is able to control and categorize room conditions based on temperature and humidity factors. The classification value using the C4.5 decision tree algorithm is 92.33% with an average temperature and humidity value of 25oC and 49%. © 2022 IEEE.
ABSTRACT
The number of Covid-19 cases has decreased, with vaccines and the implementation of health protocols. Currently, people are starting to carry out face-to-face activities such as in schools, offices, shopping centers or other public places. However, entering the new normal activities, new variants of Covid19 may be formed and spread, therefore people still have to implement and maintain the health protocols, including the use of face mask in the offices or other public places. This study design the smart room system equipped with face mask detection to ensure the implementation of health protocols and air quality smart system to monitor the CO2 levels, temperature, and humidity, as well as disinfecting the room regularly with UV-C. Face Mask Detection system is developed in Raspberry, where the images from camera are processed with machine learning using MobileNetv2 for image classification method with Python libraries TensorFlow and Keras. The implementation results show that face mask detection able to process up to five people in one frame, with a maximum distance of 220cm from the camera. Air quality monitoring system also works well in detecting and classifying air parameters with error reading is under 5% and displaying them on the website application with average delay is about 4 seconds. © 2022 IEEE.