Sari, Nadia Roosmalita, Mahmudy, Wayan Firdaus, Wibawa, Aji Prasetya and Sonalitha, Elta (2017) Enabling external factors for inflation rate forecasting using fuzzy neural system. International Journal of Electrical and Computer Engineering (IJECE), 7 (5). pp. 2746-2756. ISSN p-ISSN: 2088-8708; e-ISSN: 2722-2578

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Abstract

Inflation is the tendency of increasing prices of goods in general and happens
continuously. Indonesia's economy will decline if inflation is not controlled
properly. To control the inflation rate required an inflation rate forecasting in
Indonesia. The forecasting result will be used as information to the
government in order to keep the inflation rate stable. This study proposes
Fuzzy Neural System (FNS) to forecast the inflation rate. This study uses
historical data and external factors as the parameters. The external factor
using in this study is very important, which inflation rate is not only affected
by the historical data. External factor used are four external factors which
each factor has two fuzzy set. While historical data is divided into three input
variables with three fuzzy sets. The combination of three input variables and
four external factors will generate too many rules. Generate of rules with too
many amounts will less effective and have lower accuracy. The novelty is
needed to minimalize the amount of rules by using two steps fuzzy. To
evaluate the forecasting results, Root Means Square Error (RMSE) technique
is used. Fuzzy Inference System Sugeno used as the comparison method. The
study results show that FNS has a better performance than the comparison
method with RMSE that is 1.81.

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