Penerapan Logika Fuzzy Dalam Pemodelan Perkiraan Tingkat Inflasi Di Indonesia - Ali Muhson (Universitas Negeri Yogyakarta, 2007)
Modeling for inflation rate in Indonesia was done by some researcher with conventional model. The conventional model can not be applied if the data are linguistic variables. Fuzzy system can be used to overcome the weak of this method. The goal of this research is to establish the model for forecasting inflation rate in Indonesia based on fuzzy time series data.In this research, forecasting inflation rate use table look up scheme method based on multivariate fuzzy time series data. This research is done by the following steps: 1) determine input-output data; 2) determine fuzzification for input-output data; 3) determine fuzzy rules base by table lookup scheme method; 4) construct fuzzy inference engine; 5) construct defuzzification; 6) construct fuzzy system for modeling inflation rate in Indonesia; 7) determine validation for model used MSE criteria. The results of this research are 1) we develop 8 fuzzy models; 2) The model with Gaussian membership function, minimum inference engine, 25 fuzzy rules has minimum MSE value, 5.5671. Therefore this model can be used to predict inflation rate in Indonesia based on inflation previously , credit interest rate, money supply, gross national product and exchange rate of Rupiah.