<![CDATA[Dengue  disease  is  a  major  health  problem  and  endemic  in  several  countries  including  Indonesia.  Indonesia  is  included  in  the  category  "A"  in  the stratification of DHF by WHO in 2001 which indicates the high rate of treatment in hospital and deaths from dengue. The purpose of this study was to investigate the ability of artificial neural networks Backpropagation method for information of the spread of dengue fever in  a region. In this study uses six input variables which are environmental factors that influence the spread of dengue fever, include average temperature  -  average, rainfall, number of rainy days, the population density, sea surface height, and the percentage of larvae-free number for  which data is sourced from BMKG, BPS and the Public Health Service. Network architecture applied to a multilayer network that uses an input with 6 neurons, one hidden lay er and an output with the output neuron is one. From the results obtained by training  the best network architecture is the number one hidden layer with the number of neurons obtained a total of 110 neurons and also the system can recognize the entire training data. The best training algorithm using  a variable learning rate and momentum of 0.9 by 0.6 by the end of the training MSE 0.000999879. in the process of testing using test data obtained 17 tissue levels of  approximately 88.23% accuracy. Therefore we can conclude that the network is implemented in this study when subjected to the test  data other then the error rate of about 11.77%.Keywords : Artificial Neural Networks; Backpropagation; Dengue fever]]>
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