TY - JOUR
T1 - Support Vector Regression for Predicting the Number of Dengue Incidents in DKI Jakarta
AU - Tanawi, Ivan Noverlianto
AU - Vito, Valentino
AU - Sarwinda, Devvi
AU - Tasman, Hengki
AU - Hertono, Gatot Fatwanto
N1 - Funding Information:
This research is funded by Hibah PUTI Q3 UI No. NKB-1997/UN2.RST/HKP.05.00/2020. We would like to thank the Meteorology, Climatology, and Geophysical Agency (BMKG) and the Jakarta Health Department for their data sets, without which this research would not have been possible.
Publisher Copyright:
© 2021 Elsevier B.V.. All rights reserved.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - Dengue fever is a disease caused by the dengue virus, which is spread by Aedes aegypti and Aedes albopictus mosquitoes. According to the WHO, as a tropical country, Indonesia is a country at high risk for dengue. Dengue can spread to other people through mosquito bites. Weather factors, such as temperature, humidity, and rainfall, affect the number of dengue incidents. It is important to predict the number of dengue incidents so that the government and the people will be ready to prevent a dengue outbreak when the number of dengue incidents is predicted to be high. In this paper, we predict the number of dengue incidents in DKI Jakarta using support vector regression, with weather and the previous number of incidents as predictor variables. These predictor variables are determined by analyzing the time lag between each predictor variable and the number of incidents by using cross-correlation. Models for prediction are compared by Root Mean Squared Error and Mean Absolute Error. The result shows that support vector regression with linear kernel is quite good, and is in fact better than the radial kernel, for predicting the number of dengue incidents.
AB - Dengue fever is a disease caused by the dengue virus, which is spread by Aedes aegypti and Aedes albopictus mosquitoes. According to the WHO, as a tropical country, Indonesia is a country at high risk for dengue. Dengue can spread to other people through mosquito bites. Weather factors, such as temperature, humidity, and rainfall, affect the number of dengue incidents. It is important to predict the number of dengue incidents so that the government and the people will be ready to prevent a dengue outbreak when the number of dengue incidents is predicted to be high. In this paper, we predict the number of dengue incidents in DKI Jakarta using support vector regression, with weather and the previous number of incidents as predictor variables. These predictor variables are determined by analyzing the time lag between each predictor variable and the number of incidents by using cross-correlation. Models for prediction are compared by Root Mean Squared Error and Mean Absolute Error. The result shows that support vector regression with linear kernel is quite good, and is in fact better than the radial kernel, for predicting the number of dengue incidents.
KW - dengue
KW - machine learning
KW - prediction
KW - Regression
KW - support vector regression
UR - http://www.scopus.com/inward/record.url?scp=85101783669&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2021.01.063
DO - 10.1016/j.procs.2021.01.063
M3 - Conference article
AN - SCOPUS:85101783669
SN - 1877-0509
VL - 179
SP - 747
EP - 753
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 5th International Conference on Computer Science and Computational Intelligence, ICCSCI 2020
Y2 - 19 November 2020 through 20 November 2020
ER -
