English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Dengue fever disease is one of the public health problems in Indonesia growing rapidly and spreading widely. Dengue fever disease is caused by the dengue virus. The virus is spread by species mosquito Aides Aegypti and Aedes Alboctipus as primer vector as well as Aedes polynesiensis, Aedes scutellaris and AE (Finlaya) niveus as secondary vector. According to data from WHO, Pacific Asean bore 75 percent from dengue burden in the world during 2004 and 2010, while Indonesia is reported as the biggest second country with dengue fever disease cases between 30 endemic countries. One way to have a better understanding of the problem is by identifying the model based on the known data and do prediction. It is expected that the government would take action based on the prediction result. To solve this problem, the researcher using Bat Algorithm and artificial neural network backpropagation. In order to solve this problem. This paper purposes Bat Algorithm and Artificial Neural Network – Backpropagation to identify the spreading model. The purpose of the identification system using neural network backpropagation is to identify the ODE model of spreading dengue fever disease based on actual data. The first process is the estimation of parameters model using bat algorithm, with inquiring for a numeric solution from the ODE model spread of dengue fever disease as an objective function. The second process is model identification using artificial neural networks and the last, prediction of dengue fever spreading. T Based on the simulation result using dengue fever disease data start from January 2013 until December 2017 the MSE is 0.008 for identification process and 0,1728 for prediction process whereas The MSE value in validation process result is 0.1089 for identification process and 0.1617 for prediction process.

Model identification for prediction of dengue fever disease spreading using Bat Algorithm and backpropagation