The use of Triple Exponential Smoothing Method (Winter) in forecasting passenger of PT Kereta Api Indonesia with optimization alpha, beta, and gamma parameters

Abstract

This research aims to implement Triple Exponential Smoothing Methods (Winter) with the control parameters of alpha, beta, and gamma through techniques initialization data history with the smallest value of Mean Absolute Percentage Error (MAPE). The time series data used from Kereta Api Indonesia Ltd. (PT KAI) Bandung Indonesia between 2006 and 2014 for the Argo Wilis, Turangga, Mutiara Selatan, Pasundan, and Kahuripan trains. The results of this study indicate that for the data fifth train fleet has a pattern of non-stationary fluctuating trend. Initialization of the most well done to the data is one year to produce optimal MAPE. The results of forecasting with Triple Exponential Smoothing Methods (Winter) generally have good accuracy, namely Argo Wilis is 86.60, Turangga is 70.13, Mutiara Selatan is 85.16, Pasundan 90.87, and Kahuripan 88.47 percent. In general, the accuracy of forecasting that is used quite well.