Covid-19 is a new virus that emerged at the end of 2019 in Wuhan city, China.  This virus continues to grow until it spreads to various countries in the world.  As a result, there has been a large accumulation of Covid-19 patients in every hospital in every country affected by Covid-19.  Covid-19 patients receiving treatment in hospitals have different conditions and severity, this of course affects the different mechanism for handling patients.  Therefore, technological support is needed to help classify the treatment of patients so that they can be concentrated on patients who can be treated with isoman treatment or must be referred to hospital.  This research was conducted to build a model based on a dataset of patients infected with Covid-19 using the Naive Bayes Classifier algorithm.  The model built can predict the treatment status of patients based on age and gender who have the highest probability of being treated in an isoman way or having to be referred to hosspital. Data used is applied using Rapidminer with validation used is spill validation with the ratio of training data is 70% and test data is 30%.  The results of this research indicate classification using the Naive Bayes Classifier algorithm has a high level of accuracy in classifying patient status data, rately 83.33%.