Implementation of Naive Bayes Algorithm in Analyzing Acceptance of Poor Student Assistance

Abstract

The cost of education that is not insignificant is felt by the community, especially among the lower-middle-class people who have low-income levels. MA Hizbul Wathan NW Semaya Sikur is one of the schools where some students still have a lower-middle economic level. To overcome the existing problems the government provides assistance to poor students (BSM) in schools, but the problems felt in schools are still difficult in analyzing students who are entitled to receive BSM, therefore it is necessary to build a data processing system using the principles of data mining with the aim of helping the school in analyzing students who are entitled to receive assistance. The Naive Bayes method was chosen because it can predict future opportunities based on experience. The test results obtained were 95.27% of 169 student data with an AUC value of 1,000. This value is categorized as an excellent classification. Therefore the Naive Bayes method can be used as a reference in analyzing poor student assistance data very well.