Poverty Line Forecasting Model Using Double Exponential Smoothing Holt's Method

The research aims to forecast the Poverty Line, to help a government obtain accurate and fast information. The method used in this research is Double Exponential Smoothing Holt's Method. This method is a part of the data based on time series analysis. The research applies the forecasting theory to produce a poverty line forecast for the coming year. Next, this research is analyzing data patterns and determine the best parameter values. Double Exponential Smoothing Holt's method uses the parameters Alpha (alpha) and Gamma (gamma). To determine the best parameter value is to use the trial and error method. The best parameter value produces the smallest value of MAPE (Mean Absolute Percentage Error). The data pattern shows the trend, meaning that the Double Exponential Smoothing Holt's method is appropriate to be used in this research. The parameter values generated from the trial and error methods are Alpha (alpha) of 0.7 and Gamma (gamma) of 0.1, which produced the smallest measure of accuracy, in this research using MAPE. By observing the results of the forecasting that has been done, this forecasting model has a very good performance. Poverty Line value will keep increasing, in accordance with increasing consumption patterns and rising prices of basic necessities.