Forecasting the Number of Outpatient Patient Visits Using the ARIMA, SES and Holt-Winters Methods at XYZ Community Health Center

Author

Irfan Dwiguna Sumitra, S.Kom., M.Kom. Ph.D

Abstrak

The purpose of the research is to find out the visiting patient at the community health center. This research is Forecasting used the ARIMA method, Single Exponential Smoothing, and Holt-Winters, which are very suitable for processing data that is time series as in outpatient visits. The data from outpatients is patient visits for five years from January 2014 to December 2018 where outpatient data were taken from the total number of outpatient visits from visit data categories: General, Clinic, BPJS, Non-BPJS (SKM ), Non-BPJS (Gakinda) List of new patients and the list of patients who were then predicted the level of patient visits during the next two years, namely from January 2019 to December 2020. This study compared the best method among the three-time series methods. The forecast compared with actual data to see accuracy and find out which Forecast is best. The final results show that the MAPE value of the ARIMA method for patient visit data is worth 22.55%, the Single Exponential Smoothing method is worth 9.74%, and the Holt-Winters method is worth 7.90%. It can be said that the smallest error value is Holt-Winters from patient visit data with MAPE 7.90%, which is said to be an excellent Forecasting category by producing a total value of Forecast = 53894.2 with an average monthly = 2245.59 for Forecasting for the next two years. This monthly average result is used as a reference for the number of visitors who come for each month around 2245 people after that the last step taken is to make a strategic design using the SWOT analysis technique combined with prediction results using the Holt-Winters method in getting a conclusion that can make documents which is only around 2245 documents/person, especially outpatient data for problem solutions that occur at public health centers.