TY - GEN
T1 - Weight Optimization of the Neural Fuzzy System (NFS) Using Genetic Algorithm for Forecasting
AU - Sari, Nadia Roosmalita
AU - Mahmudy, Wayan Firdaus
AU - Wibawa, Aji P.
AU - Santika, Gayatri Dwi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Inflation is a phenomenon of increasing prices on a continuous basis which results in the increase of other goods. This study proposes the Neural Fuzzy System (NFS) as a method to predict the rate of inflation in Indonesia. To improve the accuracy, the weight at this stage of Neural Network to be determined correctly. So, this research using Genetic Algorithms to determine the best weights in the training process. This weight can be used to obtained output thorough testing process. Then, it can be processed again in the next step using FIS Sugeno until obtained the end forecasting result. To increase more accurate forecasting results, the establishment of fuzzy rules must be specified correctly. It takes a novelty that minimizes the number of fuzzy rules by dividing the initial parameter into the two (positive and negative) on stage Neural Network. So, the fuzzy rules generated less. To measure the accuracy of the system used the RMSE technique. Based on this result, the proposed method obtained for 0.89.
AB - Inflation is a phenomenon of increasing prices on a continuous basis which results in the increase of other goods. This study proposes the Neural Fuzzy System (NFS) as a method to predict the rate of inflation in Indonesia. To improve the accuracy, the weight at this stage of Neural Network to be determined correctly. So, this research using Genetic Algorithms to determine the best weights in the training process. This weight can be used to obtained output thorough testing process. Then, it can be processed again in the next step using FIS Sugeno until obtained the end forecasting result. To increase more accurate forecasting results, the establishment of fuzzy rules must be specified correctly. It takes a novelty that minimizes the number of fuzzy rules by dividing the initial parameter into the two (positive and negative) on stage Neural Network. So, the fuzzy rules generated less. To measure the accuracy of the system used the RMSE technique. Based on this result, the proposed method obtained for 0.89.
KW - forecasting
KW - inflation
KW - optimization Neural Fuzzy System
KW - Root Mean Square Error (RMSE)
UR - https://www.scopus.com/pages/publications/85077815510
U2 - 10.1109/ICOMITEE.2019.8921279
DO - 10.1109/ICOMITEE.2019.8921279
M3 - Conference contribution
AN - SCOPUS:85077815510
T3 - Proceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
SP - 175
EP - 182
BT - Proceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
A2 - Slamin, S.
A2 - Prihandoko, Antonius Cahya
A2 - Adnan, Fahrobby
A2 - Prasetyo, Beny
A2 - Nerisafitra, Paramitha
A2 - Riskiawan, Hendra Yufit
A2 - Sulistiyani, Endang
A2 - Destarianto, Prawidya
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
Y2 - 16 October 2019 through 17 October 2019
ER -