Implementation of the Naive Bayes and Simple Additive Weighting Methods in the Feasibility Selection of Village Cash Assistance Recipients

Abstract

Village Fund Direct Cash Assistance (VF-DCA) is a direct cash assistance policy in which funds come from the village. The VF-DCA policy is expected to ease the burden on the community, especially those with a low economy. Still, it is undeniable that there are opportunities for misuse of village funds by some VF-DCA organizers, intentionally or unintentionally. Problems that arise in implementing VF-DCA are mistakes in determining the beneficiary communities that are not on target so that the impact of VF can be manipulated for the interests of certain groups in several cases of social assistance programs. The solution to these problems is that this research creates a decision support system in selecting the eligibility of assistance recipients. This research used the Naïve Bayes (NB) method in selecting VF-DCA recipients and the Simple Additive Weighting (SAW) way to determine the order of eligibility scores for each candidate for VF-DCA recipients. In this study, the results of the classification process using the NB method obtained an accuracy rate of 99.36%. Hence, this is simplified in determining potential recipients of VF-DCA using the NB and SAW methods