COMPARISON OF FORECASTING ACCURACY RATE OF EXPONENTIAL SMOOTHING METHOD ON ADMISSION OF NEW STUDENTS

Abstract

Forecasting is a tool or technique used to predict or predict a value in the future by paying attention to relevant data or information, both past data or information as well as current data or information. There are several forecasting methods, one of which is the exponential smoothing method. In this study a comparison of forecasting accuracy to new student admission data in a study program at a university using the single exponential smoothing, double exponential smoothing and triple exponential smoothing methods. The best forecasting using the single exponential smoothing method is obtained when the parameter value α = 0.9 with the mean percentage error (MPE) = 0.0239, while the best forecasting using the double exponential smoothing method is obtained when the parameter value α = 0.8 and β = 1, with an MPE value of 0.1172. The best forecasting using the triple exponential smoothing method is obtained when the values of α = 0.6 and β = 0.9, with an MPE value of 0.0161. Based on the forecasting results of the three methods, it was concluded that the best forecasting obtained in this study was to use the triple exponential smoothing method with an MPE value of 0.0161.