Support Vector Regression for Predicting the Number of Dengue Incidents in DKI Jakarta

Ivan Noverlianto Tanawi, Valentino Vito, Devvi Sarwinda, Hengki Tasman, Gatot Fatwanto Hertono

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)747-753
Number of pages7
JournalProcedia Computer Science
Volume179
DOIs
Publication statusPublished - 2021
Event5th International Conference on Computer Science and Computational Intelligence, ICCSCI 2020 - Virtual, Online, Indonesia
Duration: 19 Nov 202020 Nov 2020

Keywords

  • dengue
  • machine learning
  • prediction
  • Regression
  • support vector regression

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