Abstract

One of the government's targets in achieving social development is to reduce food insecurity. Limited economic access for households to obtain sufficient food can cause food insecurity. The social protection program is a policy that plays an important role in efforts to fulfil economic access for households to reduce the incidence of food insecurity. A study about the contribution of social protection programs to food insecurity events needs to be carried out. One of the machine learning methods, namely classification tree can be applied to achieve this goal. The data used in this study is from the 2020 Indonesian Social Economic Survey (SUSENAS) in Aceh Province which consists of food insecurity status as an output variable and 7 input variables; PKH, KKS, BPNT, Local Government Assistance, BPJS, Jamkesda, and PIP. The results obtained are that BPJS provides the largest contribution in determining the status of food insecurity with an AUC value of 0.60.

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