Kebijakan Berbasis Data: Analisis dan Prediksi Penyebaran COVID-19 di Jakarta dengan Metode Autoregressive Integrated Moving Average (ARIMA)

Abstract

Data and information are important decision-making variables in handling COVID-19. Both demographic and aggregate COVID-19 data in the DKI Jakarta Province are processed and analyzed to provide information about the current situation and conditions related to the COVID-19 pandemic in the DKI Jakarta Province. The COVID-19 data also functions to perform predictive analysis in determining estimated number of COVID-19 cases in the future. The predictive analysis used in this article is the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA model is one of the forecasting methods derived from the expansion of the Autoregressive Moving Average (ARMA) model for non-stationary data. Data analysis and visualization were carried out with Python and Tableau programs, which predictive analysis results show an increasing trend of daily positive cases in the next 14 days based on the data used. This analysis may be taken into consideration by the government in creating policies and interventions in handling COVID-19 in Jakarta, and by the community to continuously take preventive actions to impede an increase in cases, such as by complying with Government-established health protocols.