Open AccessModel identification of dengue fever spreading using firefly algorithm and backpropagation neural network
Author(s) -
Safira Anggraeni Fitania,
Auli Damayanti,
Asri Bekti Pratiwi
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/546/3/032008
Subject(s) - dengue fever , backpropagation , artificial neural network , firefly algorithm , identification (biology) , computer science , java , firefly protocol , algorithm , population , artificial intelligence , machine learning , medicine , particle swarm optimization , virology , biology , zoology , botany , environmental health , programming language
Dengue Fever is one of Indonesia’s well-known medical problems where the range spread territories have became more extensive alongside with mobility and population growth. Considering that a large number of population in East Java - Indonesia has been infected, the identification of Dengue Fever is needed in order to anticipate and minimalize the terrible possibilities that could happen. The aim of this research is to obtain the result of Dengue Fever spreading model identification using Firefly Algorithm and Back-propagation Neural Network. Back-propagation Neural Network identification is proposed to estimate the spreading of Dengue Fever based on actual data. The process begins with estimating the parameters using Firefly Algorithm then identifying the model using Back-propagation Neural Network. Based on the implementation and simulation on the Dengue Fever spreading data in East Java-Indonesia from January 2013 to December 2017, model was succesfully identified where the error value between estimated data and actual data was 0.0242.