The company uses the theory of going concern where the company is considered able to maintain its business for a long period of time, with the understanding that the company will not experience bankruptcy in a short period of time (Listantri & Mudjiyanti, 2016) but it cannot always be achieved because a company can experience financial distress that can cause bankruptcy. Based on the type of data and analysis, This research uses quantitative descriptive research methods in analyzing data, because with this approach will be known the data in real terms that are shown with numbers and the truth can be accounted for. Based on the results of the analysis in the research conducted, the results of the analysis were obtained quickly and accurately, from the tests conducted by comparing training data with testing data using rapid miner support applications obtained an accuracy rate of 95.56%. The process of data mining with the naive bayes method utilizes training data to generate the probability of each criterion for different classes, so that the probability values of these criteria can be optimized for financial distress analysis of food and beverage companies