ABSTRACT
After the coronavirus outbreak, the disease known as COVID-19 has been infecting millions of people, and the number of deaths is pilling up to hundreds of thousands. In Indonesia, especially Jakarta, some of the deaths are caused by pandemic-related surges that strain hospital capacity. Besides, people had many obstacles in this pandemic condition because of the lack of knowledge about COVID-19. On that matter, several models emerged worldwide to help inform public decision making in this pandemic situation. With today's technological advances the CHIME (COVID-19 Hospital Impact Model for Epidemics) application is designed to assist hospitals and public health officials with understanding hospital capacity needs as they relate to the COVID pandemic. This paper aims to help inform public health decision making regarding the transmission of COVID-19 in Jakarta using CHIME. This work uses Jakarta COVID-19 data from November 24th, 2021 and its accumulation from 14 days before (November 10th, 2021) to predict the course of COVID-19 in 30 days. With ArcGIS Pro and ArcGIS Experience, this work successfully made a map that uses CHIME to inform about peak demand of each city in DKI Jakarta and the daily new admissions and hospitalization graph. In addition, a Jakarta COVID-19 dashboard is also made to inform more about the transmission of COVID-19. © 2022 IEEE.
ABSTRACT
COVID-19 has majorly impacted the world and has spread to every corner of the world. As a result, the tourism industry suffered greatly with many tourist sites having to close. Previous research has used regression models to predict the impact of COVID-19, though few has linked it to the number of tourists. This paper uses five different regression models to predict tourism rates based on multiple country's COVID-19 data. Regression models include linear regression, polynomial regression, K-Nearest Neighbors regression, random forest regression, and support vector regression. The datasets that we use are COVID-19 data that contains the number of cases and Indonesia's tourism data that contains the monthly number of incoming tourists to Indonesia from different countries. The dataset will be processed by selecting the countries with the most amount of tourist. The preprocessed dataset is divided into two for training and testing the models with an 8:2 ratio. The result from the evaluation showed that random forest regression has the highest accuracy with a R2 score of 0.9. Our research is limited to the number of datasets that are used as there might be other variables that are not considered. © 2022 IEEE.