This study aims to predict the nuAmbbsetrr aocft poor in Indonesia for the next few yearsusing a triple exponential smoothing method.The purpose of this research is the result of the forecast number of poor people inIndonesia accurate forecast results are used as an alternative data the government forconsideration of government to determine the direction of national poverty reductionpolicies. This research includes the study of literature research, by applying the theory offorecasting to generate predictions of poor people for coming year. Furthermore, analyzingthe mistakes of the methods used in terms of the count: Mean Absolute Deviation (MAD),Mean Square Error (MSE), Mean absolute percentage error (MAPE) and Mean PercentageError (MPE). The function of this error analysis is to measure the accuracy of forecastingresults that have been conducted.These results indicate that the number of poor people in 2017 amounted to24,741,871 inhabitants, in 2018 amounted to 24,702,928 inhabitants, in 2019 amounted to24,638,022 inhabitants and in 2020 amounted to 24,547,155 people. The forecasting resultsshow an average reduction in the number of poor people in Indonesia last five years (2016-2020 years) ranges from 0.16 million. Analysis forecasting model obtained an mean absolutedeviation (MAD) obtained by 0.246047. Mean squared error (MSE) of forecasting resultswith the original data by 1.693277. Mean absolute percentage error (MAPE) of 3.040307%and the final Mean percentage error (MPE) of 0.888134%.