Airplane Passenger Prediction Model at Jendral Ahmad Yani Airport, Semarang

Abstract

The prediction model for airplane passengers at General Ahmad Yani Airport in Semarang is very important, especially for airport managers, so that the infrastructure at the airport can be facilitated by managers according to the number of passengers available. Airplane passengers in this case include arriving and departing passengers. The prediction model is differentiated between before and after the COVID-19 pandemic, where passenger fluctuations during the Covid pandemic saw a significant decrease in passengers and after normal times there was a significant increase. Due to limited existing data, the independent variable uses GDP, because GDP is considered to represent economic growth and regional economic structure. The airport service area is limited by the presence of other airports close to the airport under study. The model is planned using Artificial Neural Networks (ANN) but if the data cannot be processed then the model is analyzed using Regression. The prediction model will later compare before and after post-Covid. Analysis of existing data shows that the passenger prediction model used in the future is a post-COVID-19 prediction model, the results of which are better applied with other possible independent variables if any.