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Management of medical actions carried out in handling patients who are ODP (people under monitoring), OTG (asymptomatic people), PDP (patient under monitoring) and positive Covid-19 patients is carried out based on assumptions, such as self-isolation, hospitalization, or special treatments in the ICU (Intensive Care Unit) room. The condition of the body in each patient is different, a patient may have same symptoms but the treatment is different, especially in elderly patients. Many problems occur in determining medical action because the patient's body condition is different. Therefore, it needs to be appointed as a research. The research method used in this study was Nive Bayes algorithm with supporting application Rapid Miner. It was applied to carry out the process of testing on patient data as much as 500 data, 25 variables or patient symptoms and 3 outputs as a form of medical action. Based on the results of the analysis carried out in this study, prediction of medical actions for ODP, PDP, OTG and positive Covid-19 patients were obtained by comparing training data with testing data using Rapid Miner application. It resulted that an accuracy rate of 76.00% was obtained

This research was carried out by comparing the DS18B20 sensor and the MLX 90614 sensor to detect the accuracy of detecting human body temperature which is used to detect Covid 19 symptoms. In this experiment, 10 trials were carried out with different human segments detected using the sensor. In the experiment, it was found that the MLX90614 sensor is more suitable to be used for development with an IoT-based system because it does not need to come into contact with the skin of the human body. The MLX90614 sensor will detect the temperature and change it to one Celsius unit then send it to the firebase database which will then be picked up by the android application which is held by the security officer so that the temperature can be known remotely. When the temperature is more than 38.5 Celsius it will turn on the buzzer sound which can be heard from a distance which indicates the temperature is above 38.5 Celsius and on android will also display a danger sign