BibTex Citation Data :
@article{JSINBIS6062, author = {Didi Supriyadi and Kusworo Adi and Eko Sarwoko}, title = {Sistem Informasi Penyebaran Penyakit Demam Berdarah Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation}, journal = {Jurnal Sistem Informasi Bisnis}, volume = {1}, number = {3}, year = {2011}, keywords = {}, abstract = { 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 }, issn = {2502-2377}, pages = {159--167} doi = {10.21456/vol1iss3pp159-167}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/24} }
Refworks Citation Data :
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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Diagnosis classification of dengue fever based on Neural Networks and Genetic algorithms
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