Seleksi Penerima Bantuan Pangan Non Tunai di Desa Menggunakan Metode Naïve Bayes dan Simple Additive Weighting

Abstract

Poverty is one of the problems experienced by some developing countries, including Indonesia. There are many ways to mitigate poverty, for example, Indonesian government policy overcome this situation by Non-Cash Food Aid Program (BPNT). The electoral candidate for BPNT in the rural area is carried out by Poverty Reduction Team (SATGASKIN). To avoid uneven and untargeted assistance with the process, a system is capable for addressing the matter. The selection methods in this research were Naive Bayes and Simple additive weighting. The purpose of this research was to design and build an application that provided convenience to SATGASKIN in determining the eligibility of prospective beneficiaries and prioritizing beneficiaries. As a result of the study, the system can be used by SATGASKIN to help determining the recipients’ eligibility with 85% accuracy value, 85.71% Precision, and 92.31% Recall. Naive Bayes and Simple Additive Weighting (SAW) methods reached 100% according to the results by manual calculations.