Classifying the Poor Household Using Neural Network

The issue of poverty has recently been brought to the public’s attention. The picture of Indonesian development reveals many families are not benefiting from national economic growth. Many families were still poor and hovering below the poverty line. The classification of the individual or of poor households in a class or poverty status can be a good instrument to focus on the living conditions of the poor. In this study, backpropagation algorithm was used to build models of neural networks that can classify each poor household appropriate their poverty status. Network built using the weights of the selection of the best network. The best networks have been training on the sub-sub smaller dataset. Classification is done by replication 10-fold cross validation. Average accuracy of classification in the training data is 58.89 percent while the testing data of 56.42 percent. Keywords : Backpropagation, Classification, Neural network, Poverty.