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The Customs Supervision and Service Office is given a revenue target that must be achieved annually. However, revenue at the Customs Supervision and Service Office tends to fluctuate because it is strongly influenced by various external factors that are difficult to predict. Projections need to be done to see if the given revenue target can be achieved. This study aims to conduct forecasting so that it can be estimated how much revenue will be at the end of the year (December 2023). Research is conducted using the Grey(1,1) and Grey-Markov(1,1) models. The analysis results show that the Grey-Markov(1,1) model provides better forecasting accuracy compared to the Grey(1,1) model with a MAPE value of 5.390541% and a Posterior Error Ratio of 0.190644. These results show that the Grey Markov(1,1) model is more accurate than the Markov(1,1) mode, and that this method (Grey Markov(1,1)) is very good for forecasting with little data.