SARIMA-GLS-ANN hybrid model for forecasting airplane passengers.

After Covid-19 pandemic, there has been no research that has modeled the number of plane passengers by considering the influence of seasonal effects. The purpose of the research is to predict domestic air passengers' traffic at Juanda Airport after the Covid-19 pandemic. The forecasting model used in this research is using the SARIMA-GLS-ANN hybrid model. SARIMA is used to capture linear data patterns while the GLS method excels in determining parameters. ANN excels in modeling data resulting from non-linear processes. The combination of linear approach in SARIMA, parameter estimation in GLS and nonlinear in ANN can be applied. A sample of 48 time series data from January 2019 to December 2022 was processed with the SARIMA-ANN method on SPSS 25, obtained valid data as many as 36 samples with 25 samples of training data (69.4%) and 11 samples of testing data (30.6%). The result of the difference in relative error in training data and testing data on SARIMA-ANN is 42.39%. Furthermore, data processing with the SARIMA-GLS-ANN method on SPSS 25 obtained valid data as many as 35 samples with training data as many as 23 samples (65.7%) and testing data as many as 12 samples (34.3%). The result of the relative error difference in training data and testing data on SARIMA-GLS-ANN is 4.29%. The SARIMA-GLS-ANN method is better at solving forecasting or predict domestic air passengers' traffic at Terminal I of Juanda Airport Surabaya. [ABSTRACT FROM AUTHOR]