COVID-19 is easy to transmit from one infected person to a susceptible person through droplets. Human mobility and weather variable become the factors affecting COVID-19. However, the most influence variable needs to be investigated to effectively control COVID-19 spread. This paper studied the correlation between COVID-19, community mobility and weather variability in Java Island. We used the confirmed cases of COVID-19, community mobility data and weather data from the beginning of March 2020 until the end of February 2021 in each province of Java Island. Two decision tree-based models (Random Forest and XGBoost) in four experimental setups were implemented in this paper. We found that there is similarity trend between Random Forest and XGBoost method in prediction results. The performance of both has also no significant difference. The Capital City of Jakarta, Banten and the Special Region of Yogyakarta shows the best prediction result in the third experiment which used the community mobility variable as features. While, West Java shows the best result with a combination of all weather variables and mobility, Central Java and East Java with the combination of temperature and mobility. This shows that the community mobility gives an impact on COVID-19 cases in all provinces. The correlation analysis found that the community mobility percentage change in transit stations has a significant role in predicting COVID-19 cases. Based on the model performance, the prediction of COVID-19 cases in the Capital City of Jakarta has the best result. While the Special Region of Yogyakarta has the highest error.