Classification of Social Assistance Recipients Using Machine Learning

Abstract

Social assistance is assistance funds provided by local governments. In the Minister of Home Affairs Regulation No. 32 of 2011, it is explained that social assistance is the provision of assistance in the form of money/goods from local governments to individuals, families, groups, and communities which is not continuous and selective in nature. One of the villages in North Lampung still often experiences problems, including high poverty rates and low education levels. The Naive Bayes algorithm method was chosen to classify aid recipients based on employment, age, and income. The spreadsheet calculations show that the Family Hope Program Assistance (PKH) class is 135 people and the Direct Cash Assistance (BLT) class is 39 people with a total of 176 people in the social assistance recipient data. From the results of Rapid Miner calculations, the accuracy value for the PKH and BLT classes is 100.00%.