<![CDATA[Air transport is the first choice for people who travel long distances. Forecasting is important in the decision-making process, especially for air transportation which can provide information on the increase and decrease in passengers that fluctuate. To describe the fluctuation of a data that changes rapidly over time results in the variance of error changing over time, so the data is estimated to be heteroscedasticity. ARCH and GARCH methods are useful for modeling heteroscedasticity elements in data. The following research includes applied research, using data on the number of passengers arriving on airplanes in 2018 –2022. From the results of the study, the best model according to the lowest AIC value was obtained the GARCH model (1.2). The variance equation in the GARCH model (1,2) is . Then using the GARCH model (1.2), forecasting was carried out in 2018 – 2022, which was as many as 60 data. From the forecasting results obtained the number of passengers came the aircraft.]]>
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