ANALISA KOMPARASI NEURAL NETWORK BACKPROPAGATION DAN MULTIPLE LINEAR REGRESSION UNTUK PERAMALAN TINGKAT INFLASI - Amrin Amrin (JURNAL TEKNIK KOMPUTER AMIK BSI, 2016)
The inflation rate can not be underestimated in a country's economic system and businesses in general. If inflation can be predicted with high accuracy, of course, can be used as the basis of government policy making in anticipation of future economic activity. In this study will be used back propagation neural network method and multiple linear regression method to predict the monthly inflation rate in Indonesia, then compare which method is the better. The data used comes from the central statistical agency in 2006-2015, which is 80% as training data and 20% as testing data. In the results of the data analysis is concluded that the performance of multiple linear regression is better than back propagatin neural network, with a mean absolute deviation (MAD) is 0.0380, a mean square error (MSE) is 0.0023, and a  Root Mean Square Error (RMSE) is 0.0481. Keywords: Inflation, neural network backpropagation, multiple linear regression, mean square error.