Decision Support System for Covid19 Affected Family Cash Aid Recipients Using the Naïve Bayes Algorithm and the Weight Product Method

Abstract

The purpose of this study was to predict the recipients of cash assistance and to evaluate Naïve Bayes in predicting recipients of cash assistance from families affected by Covid19. This study uses the Naïve Bayes algorithm to calculate the accuracy and classification of cash transfer recipient data. The data used is logical data then processed and calculated. The variables used are Age, Income, College Status, and labels using two classes, namely Cannot and Can. From the results of this study, it can be concluded that the recipients of cash assistance in Village X can be predicted Naïve Bayes using a training value of 10%. Based on the results of the evaluation using confusion matrix and testing the accuracy of Naïve Bayes is 67%. For the calculation of the Weighted Product method using variables Age, Income, Education, Working Status, Family Status and there are two alternatives, namely Cannot and Can. From the Weighted Product calculation, it produces a Vector S ranking value of 2.24 and Vector V of 0.66, which states that families affected by Covid19 have the right to receive cash assistance.