Implementasi Gabungan Metode Multi-Factors High Order Fuzzy Time Series dengan Fuzzy C-Means untuk Peramalan Tingkat Inflasi di Indonesia - Jefri Hendra Prasetyo; Agus Wahyu Widodo; Bayu Rahayudi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Inflation is a monetary phenomenon in a country where ups and downs result in economic turmoil. Bank Central Indonesia sets the inflation target for the next time with the Inflation Targeting Framework (ITF) as a reference for monetary policy. If the actual inflation does not match the inflation target, then the policy is needed to return inflation to such an inflation target. Based on the inflation rate problem, this research is expected to provide inflation target for the future through inflation rate forecasting using combined Multi-Factors High Order Fuzzy Time Series method with Fuzzy C-Means. Fuzzy C-Means is used to determine the cluster center to be used as a basis for the development of intervals, the use of Fuzzy C-Means is expected to reflect the real data so that the results of forecasting is better. In forecasting used 4-factor data that includes time series data rate inflation and 3 factors that affect. The results of the combined implementation of Multi-Factors High Order Fuzzy Time Series method with Fuzzy C-Means tested the error of forecasting using Mean Absolute Percentage Error (MAPE). Based on the test the error value is 11.33676%, which indicates that the combined method of Multi-Factor High Order Fuzzy Time Series with Fuzzy C-Means is included in the good category used in forecasting the inflation rate in Indonesia because it has an accuracy value below 20%.