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Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method


 
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1. Title Title of document Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method
 
2. Creator Author's name, affiliation, country Mursyidatun Nabilah; Institut Teknologi Sepuluh Nopember; Indonesia
 
2. Creator Author's name, affiliation, country Raras Tyasnurita; Institut Teknologi Sepuluh Nopember; Indonesia
 
2. Creator Author's name, affiliation, country Faizal Mahananto; Institut Teknologi Sepuluh Nopember; Indonesia
 
2. Creator Author's name, affiliation, country Wiwik Anggraeni; Institut Teknologi Sepuluh Nopember; Indonesia
 
2. Creator Author's name, affiliation, country Retno Aulia Vinarti; Institut Teknologi Sepuluh Nopember; Indonesia
 
2. Creator Author's name, affiliation, country Ahmad Muklason; Institut Teknologi Sepuluh Nopember; Indonesia
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Dengue fever; Dengue forecast; Ensemble forecasting;
 
4. Description Abstract

Dengue fever is still a crucial public health problem in Indonesia, with the highest case fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-Net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38. 

 
5. Publisher Organizing agency, location Institute of Advanced Engineering and Science
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2023-03-01
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier https://ijai.iaescore.com/index.php/IJAI/article/view/21070
 
10. Identifier Digital Object Identifier (DOI) http://doi.org/10.11591/ijai.v12.i1.pp496-504
 
11. Source Title; vol., no. (year) IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 1: March 2023
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions Copyright (c) 2022 Institute of Advanced Engineering and Science
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