PEMODELAN JUMLAH KASUS DEMAM BERDARAH DENGUE DENGAN MENGGUNAKAN MODEL AUTOREGRESSIVE DISTRIBUTED LAG

Abstract

This study aims to model dengue hemorrhagic fever (DHF) cases with an autoregressive distributed lag (ARDL) model to investigate significant predictor variables in Bojonegoro Regency. The selected predictor variables are the percentage of poverty, population, health facilities, and health workers. A research design with a quantitative approach was used to investigate the predictor variables in dengue cases with the ARDL model and the help of EViews. Stationarity, cointegration, classical assumptions, parameter significance, and model goodness assessment, namely R-square, MSE, AIC, and SBC, were tested. The research data source is secondary data, namely annual data from the reports of the Central Bureau of Statistics and the Health Office in Bojonegoro Regency from 2008 to 2022. The test results show only cointegration in the response variable, so the ARDL model is applied, but the lag distribution is only done on the response variable. In testing the significance of the parameters, it was found that an increase in the health workforce significantly affected the decrease in the number of DHF patients. At the same time, the other predictor variables were not significant

Keywords

• asumsi klasik
• kointegrasi
• stasioneritas
• model ardl
• demam berdarah dengue
• classical assumptions
• cointegration
• stationarity
• ardl model
• dengue fever