PREDICTION MODEL FOR THE NUMBER OF ARI CASES IN CHILDREN IN SURABAYA USING ARIMA METHOD

Abstract

Forecasting is an important element in planning decision-making related to estimating future events. Forecasting techniques that are often developed and used today are the Time Series. Time series is a measurement of events through the stages of time in hours, days, months, and years format. This research uses the ARIMA time series method. The ARIMA method is used to model acute respiratory infections (ARI) in children. The best model is determined using the smallest error through the Mean Absolute Percentage Error (MAPE). The study aims to predict the number of ARI cases in children in Surabaya. This research is an unobtrusive/nonreactive research. The researcher conditioned the subjects to not being aware that the subject is being studied and therefore, left the subject uninterrupted. The data used was the number of ARI cases in children from January 2014 to December 2018. The data was obtained from the monthly report of the Health Information System Unit (HIS) of the Surabaya Health Office. The conclusion from this study showed that the ARIMA method obtained the best model results, namely ARIMA (2,1,2) with a MAPE value of 15.024. Forecasting results fluctuated and a downward trend in the case of ARI children in Surabaya. In certain months, the number of acute respiratory infections has increased significantly, including in February and March.