Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 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 | |
| 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![]() This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |
