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
Note: This article has supplementary file(s).
Article Metrics:
Last update:
Last update: 2025-07-15 05:54:30
Diagnosis classification of dengue fever based on Neural Networks and Genetic algorithms
Authors who submit the manuscripts to Journal JSINBIS must understand and agree that if the manuscript is accepted for publication, the copyright of the article belongs to JSINBIS and Diponegoro University as the journal publisher.
Copyright includes the exclusive right to reproduce and provide articles in all forms and media, including reprints, photographs, microfilm and any other similar reproductions, as well as translations. The author reserves the rights to the following:
JSINBIS and Diponegoro University and the Editors make every effort to ensure that no false or misleading data, opinions or statements are published in this journal. The content of articles published in JSINBIS is the sole and exclusive responsibility of the respective authors.
Copyright transfer agreement can be found here: [Copyright transfer agreement in doc] and [Copyright transfer agreement in pdf].
JSINBIS (Jurnal Sistem Informasi Bisnis) is published by the Magister of Information Systems, Post Graduate School Diponegoro University. It has e-ISSN: 2502-2377 dan p-ISSN: 2088-3587 . This is a National Journal accredited SINTA 2 by RISTEK DIKTI No. 48a/KPT/2017.
Journal JSINBIS which can be accessed online by http://ejournal.undip.ac.id/index.php/jsinbis is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats
