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Long Short Term Memory(LSTM) is known as optimized Recurrent Neural Network(RNN) architectures that overcome the lack of RNN-s about maintaining long period of memories information. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI(Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product(GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using Long Short Term Memory Method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value. In the interest of predictive accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent(sgd), Root Mean Square Propagation(RMSProp), Adaptive Gradient(AdaGrad), Adaptive moment(Adam), Adadelta, Nesterov Adam(Nadam) and Adamax. The result indicate that Nesterov Adam has 0.069 MSE-s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.