TY - GEN
T1 - Comparison of ANFIS and NFS on inflation rate forecasting
AU - Sari, Nadia Roosmalita
AU - Wibawa, Aji P.
AU - Mahmudy, Wayan Firdaus
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Inflation is very influential on the national economy. The monetary crisis can occur if inflation is not well controlled. For that, it takes a forecasting. Inflation rate forecasting can predict future country situation based on historical data. This study proposes two hybrid Fuzzy logic-Neural network methods to predict inflation rate in Indonesia. Adaptive Neuro Fuzzy Inference System (ANFIS) and Neural Fuzzy System (NFS) were chosen because both methods are hybrid Fuzzy logic-Neural network with different architecture. This study aims to find the method that has the best performance. Time series data and some external factors (CPI, Money Supply, BI Rate, Exchange Rate) are used as parameters. Proper Neural Network (NN) architecture must be found to produce high accuracy. Therefore, some tests (learning rate, epoch, neuron) are performed. The best method is chosen based on the level of accuracy produced by using Root Mean Square Error analysis technique. The results show that NFS has better performance with accuracy (RMSE=1.213) than ANFIS.
AB - Inflation is very influential on the national economy. The monetary crisis can occur if inflation is not well controlled. For that, it takes a forecasting. Inflation rate forecasting can predict future country situation based on historical data. This study proposes two hybrid Fuzzy logic-Neural network methods to predict inflation rate in Indonesia. Adaptive Neuro Fuzzy Inference System (ANFIS) and Neural Fuzzy System (NFS) were chosen because both methods are hybrid Fuzzy logic-Neural network with different architecture. This study aims to find the method that has the best performance. Time series data and some external factors (CPI, Money Supply, BI Rate, Exchange Rate) are used as parameters. Proper Neural Network (NN) architecture must be found to produce high accuracy. Therefore, some tests (learning rate, epoch, neuron) are performed. The best method is chosen based on the level of accuracy produced by using Root Mean Square Error analysis technique. The results show that NFS has better performance with accuracy (RMSE=1.213) than ANFIS.
KW - Adaptive Neuro Fuzzy Inference System (ANFIS)
KW - forecasting
KW - inflation
KW - Neural Fuzzy System (NFS)
KW - RMSE
UR - https://www.scopus.com/pages/publications/85049577434
U2 - 10.1109/ICEEIE.2017.8328775
DO - 10.1109/ICEEIE.2017.8328775
M3 - Conference contribution
AN - SCOPUS:85049577434
T3 - Proceeding - 2017 5th International Conference on Electrical, Electronics and Information Engineering: Smart Innovations for Bridging Future Technologies, ICEEIE 2017
SP - 123
EP - 130
BT - Proceeding - 2017 5th International Conference on Electrical, Electronics and Information Engineering
A2 - Afandi, A.N.
A2 - W., Aji Prasetya
A2 - Fadlika, Irham
A2 - Sendari, Siti
A2 - Handayani, Anik Nur
A2 - Widiyaningtyas, Triyanna
A2 - Lestari, Dyah
A2 - Dewi, Andriana Kusuma
A2 - Soraya, Dila Umnia
A2 - Amalia, Risky
A2 - D.W., Eka Putri
A2 - M, Pamela Paula
A2 - Wibowo, Fauzy Satrio
A2 - Agustin, Whyna
A2 - Kusumo, Gradiyanto Radityo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Electrical, Electronics and Information Engineering, ICEEIE 2017
Y2 - 6 October 2017 through 8 October 2017
ER -