Forecasting The Frequency Of Domestic Air Passengers At Juanda Airport Using Arima And Transfer Function As A Basis For Future Development Of Airport Scenario

Ary Miftakhul Huda, Heri Kuswanto, Suhartono Suhartono


In this modern age, the increase of human population is proportional to the need for airport development services, including air transportation. In Indonesia, the development of air transportation services can be seen from the increase of both passenger numbers and goods transported during the last 5 years, including at Juanda Airport in East Java. The aim of this research wasto create an airport scenario based on the number of forcasted domestic air passengers. Globally, air passenger forcasting has been one of the most important policy tools used by decision makers. The growth in air passenger numbersin the future can be forecasted based on a time series analitical approach such as multivariate time series, for example the transfer function model as multivariate time series, and ARIMA Box Jenkins as univariate time series . In transfer function method, the input series is the number of aircrafts, inflation, oil prices, while the output series is the number of passengers. Based on Outsample criteria, the best model to forecast the number of domestic passengers at the Juanda airport is univariate time series models, ARIMA(1,1,0)(1,0,0) 12 due to its minimum RMSE values. The best model obtained implied that the forecastednumber of domestic passengers at Juanda airport this month was related to the number of passengers at 1, 2, 12, 13, 14 months earlier. The result of this research indicatedthat theforecastedvalue of domestic air passengers at Juanda airport for 5 years with ARIMA(1,1,0)(1,0,0) Jurnal Tata Kota dan Daerah Volume 6, Nomor 1, Juli 2014 21 12 , ie from January 2012 to December 2016, would increase gradually each year. Therefore, PT Angkasa Pura 1 should make a scenario to expand the capacity of aiports in order to accommodatethe increase of air passengers in the future, expecially at Juanda Airport.

Keywords: passengers, aircraft, inflation, oil prices, transfer function

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