PEMODELAN JUMLAH KASUS COVID-19 DI INDONESIA DENGAN PENDEKATAN REGRESI POISSON DAN REGRESI BINOMIAL NEGATIF - Nadhifan Humam Fitrial; Akhmad Fatikhurrizqi (Politeknik Statistika STIS, 2020)
The COVID-19 pandemic has spread throughout the world, including Indonesia. It is necessary to know the various factors that affect the spread of COVID-19 cases so that both the government and the community can make prevention and response efforts so that this pandemic does not spread further. This study aims to model the number of COVID-19 cases in Indonesia and then determine the variables that have a significant effect on them. The model used is Poisson regression and Negative Binomial regression. The two models were chosen because they are a model that is often used for count data such as the number of COVID-19 cases. Then from the two models, the best model will be selected along with the variables that have a significant effect on the number of COVID-19 cases in Indonesia. The unit of analysis in this research is all provinces in Indonesia which consists of 34 provinces. The response variable in this study is the cumulative number of COVID-19 cases in Indonesia on April 9, 2020 which were compiled from Gugus Tugas Percepatan Penanganan COVID-19. This date was chosen because it was the date before the implementation of the PSBB policy for the first time in Indonesia and this study did not take government intervention variables in modeling the cumulative number of COVID-19. The predictor variables in this study were population density, the percentage of the elderly population, the percentage of households with access to improper sanitation, and the percentage of illiteracy rates for the population aged 15 years and over. Based on the AIC value, the Negative Binomial regression is better used to model the number of COVID-19 cases in Indonesia than the Poisson regression. In the Negative Binomial regression model, the population density variable and the percentage of the elderly population have a positive and significant effect on the number of COVID-19 cases in Indonesia.