T he rise of low-cost airline ticket sales carried out by many airlines in Indonesia, has causedheavy air traffic at Indonesian airports, especially at Soekarno-Hatta International Airport.This become a problem for PT Angkasa Pura II (Persero). The high demand for airlinerequests for PT Angkasa Pura II (Persero) in airport expansion or the addition of a numberof terminals at Soekarno-Hatta International Airport, led to the need for PT Angkasa PuraII (Persero) to obtain information about the development of air transport data in thefollowing period. There are several methods that can be used in forecasting air transportdata in the coming period, one of the method is time series. Because the data to be forecastis seasonal, the methods used are Holt-Winters exponential smoothing and Decompositionwhich are forecasting techniques from the time series method. The data used in this paperis air transport data at Soekarno-Hatta International Airport in 2013 - 2017 and forecastingwill be carried out to produce data from 2018 – 2020. Processed historical data is carriedout using the Extract, Transform, and Load (ETL) using tools Spoon Pentaho DataIntegration.While forecasting will be done using the R language and RStudio applicationand graphical visualization of the results of forecasting data using Microsoft Power BI.Each method will be compared with the level of accuracy of the forecasting by calculatingthe error numbers of Root Mean Squared Error (RMSE). From the results of processedprediction data, RMSE value generated in Decomposition method is smaller than Holt –Winters method.While forecasting will be done using the R language and RStudio applicationand graphical visualization of the results of forecasting data using Microsoft Power BI.Each method will be compared with the level of accuracy of the forecasting by calculatingthe error numbers of Root Mean Squared Error (RMSE). From the results of processedprediction data, RMSE value generated in Decomposition method is smaller than Holt –Winters method.