Optimization of Exponential Smoothing Method Using Genetic Algorithm to Predict E-Report Service

Abstract

Exponential Smoothing methods are proposed in this research to predict the number of loss reports in the E-Report contained on “One-Click Service Police Resort” for Banyuwangi society. The best prediction is obtained based on smallest value of the Mean Absolut Deviation (MAD), the Mean Square Error (MSE), and the Mean Absolute Percentage Error (MAPE) to select an appropriate forecasting model using Single ES (Exponential Smoothing), Double ES, and Triple ES. However, the determination of α, β and γ parameter is still manual. Genetic Algorithm method is used to set the values optimally to overcome these problems. The result from this experience show that the Single ES is determined as the best prediction method as a result of the prediction of loss report on E-Report Police Resort based on the alpha value obtained from the genetic algorithm method.