FORECASTING NUMBER OF CASES OF ACUTE RESPIRATORY INFECTION (ARI) IN 2019 USING ARIMA METHOD

Abstract

The ARIMA method is an approach that forms the most powerful model in analyzing time series data, and the studies given are very thorough. This method can be modeling data stationary or not stationary, it can be seen from sine wave shape of the plot ACF. This method is used because obtained the results are better and more accurate. According to WHO, Acute Respiratory Infection (ARI) is an infectious disease that causes can be morbidity and mortality. A four million people die each year. This study used secondary data so that it is categorized as non reactive research. The population were cases of Acute Respiratory Infections (ARI) at Jagir Health Center Surabaya which were recorded in 2013 to 2018 (monthly). The dependent variable is the cases of Acute Respiratory Infection (ARI), while the independent variable is time. The model that was obtained from the ARIMA method is a model (2.0,1). The forecasting result is 354 cases in 2019, the forecasting has increased from 2018 to only 313 cases. It was a suggestion that the forecasting result can be a reference for developing a policy and a new program or improvement in previous program so that the number cases of ARI at the Jagir Health Center can be resolved properly.