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The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This study aims to analyze inflation in Indonesia after the goverment raised the price of electricity basic and fuel price in 2013 using kernel regression models. This method was good for data modeling inflation in Indonesia before. The goodness of a kernel regression model is determined by the chosen kernel function and wide bandwidth used. However, the most dominant is the selection of the wide bandwidth. In this study, determination of the optimal bandwidth by minimizing the Generalized Cross Validation (GCV).

By model the annual inflation data (Indonesia) December 2006 - December 2011, the inflation target in 2012 is (4,5 + 1 )% can be achieved both exactly and predictly, while the inflation target in 2013 is (4,5 + 1 )% cannot be achieved neither exactly nor predictly. The inflation target in 2013 can’t be achieve because since the beginning of 2013, there was a government policy to raise the price of electricity and the middle of 2013, there was an increase in fuel prices. The prediction of Indonesia inflation in 2014 by Gauss kernel is 6,18%.

Keywords: Inflation, Kernel Regression Models, Generalized Cross Validation

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