Non-Communicable Diseases (NCDs) are diseases that cannot be transmitted from one individual to another, but depend on the individual's lifestyle. This shows that currently, non-communicable diseases are very important to be aware of, detected early, and prevented from getting worse and causing various complications and even death. About 80 percent of these deaths occur in middle- and low-income countries. 73% of deaths are currently caused by non-communicable diseases, 35% of them due to heart and blood vessel diseases, 12% due to cancer, 6% due to chronic respiratory diseases, 6% due to diabetes, and 15% due to other NCDs. The aim of the service is to provide education and health checks for PTM. The methods used in this community service are screening and education about non-communicable diseases. The potential result of this community service activity is an increase in community participation in PTM. It is hoped that this activity can be the first step in collaboration between community health centers, educational institutions and village officials in preventive efforts to prevent non-communicable diseases.

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