Using Regression to Predict Number of Tourism in Indonesia based of Global COVID-19 Cases
Eduardo Brilliandy, Henry Lucky, Adrian Hartanto, Derwin Suhartono, Muhammad Nurzaki · 2022
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.