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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Sri Herawati
University of Trunojoyo Madura
Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Combined EEMD and ANN improved by GA for tourist visit forecasting Muhammad Latif; Sri Herawati
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3566

Abstract

This study has proposed forecasting tourist visits use an ensemble empirical mode decomposition (EEMD) and optimized artificial neural networks (ANN) using genetic algorithms (GA). The data used is monthly data on tourist visits in Sumenep Regency. The data was obtained from the Sumenep district government from January 2015 to December 2019. EEMD algorithm breaks down tourist visit data into several intrinsic mode function (IMF) and residues. Then, EEMD results was normalized and then learned using ANN. GA is used to optimize weight and bias of the ANN. Experiments carried out to analyze performance in forecast results of proposed method compared with the EEMD-ANN without optimization of the GA. The experimental results show that the proposed method has better performance, namely the error value is reduced by 37%, 21% for MSE, RMSE, respectively.
Co-Authors Aeri Rachmad Fifin Ayu M Fitri Damayanti Imamah Imamah M Latif