AIRPORT PASSENGER DEMAND FORECASTING USING RADIAL BASIS FUNCTION NEURAL NETWORKS: JUANDA INTERNATIONAL AIRPORT CASE

• Main Author: MUBARAK, TITO
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2015
• Subjects: Design Practice and Management; Design Innovation
• Online Access: https://repository.ugm.ac.id/134761/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=77857
• Institution: Universitas Gadjah Mada

Forecasting is a tool to determine not only whether a new terminal building and runways are required but also how big, how many and when they are required. However, forecasting often faulty because of uncertainties. Since then, addressing uncertainties becomes the greatest challenge in forecasting. Moreover, forecasting is a part of process of converting uncertainties into qualified risks. Therefore, this study was held to search a new method in airport passenger forecasting. A method namely Radial Basis Function Neural Network is introduced as an airport passenger demand forecasting tool in the Juanda Airport. The airport has important role since it is the second largest airport in term of annual passenger in Indonesia. In addition, the current annual passenger traffic of the airport has surpassed its basic capacity by 42 % in 2013. The research was initiated with an extensive literature study on artificial neural networks, time series and causal forecasting, airport master planning, and macro economy. The literature study of artificial neural networks, time series and causal forecasting are needed to construct how the process of forecasting has to be developed. The airport master planning and macro economy study are needed in order to analyze entirely the forecasting results. Secondary data are collected from several sources such as PT. Angkasa Pura I, Indonesia Ministry of Transport, Biro Pusat Statistik, Bank Indonesia and The World Bank. The mean squared error (MSE) ranges between 0.2% and 16% during training and testing processes. The optimum result shows that the Radial Basis Function Neural Networks gained error below 1%. It means that this method is highly recommended to be utilized in the Juanda Airport. The analyses show that additional capacity is urgently needed to satisfy the current and future demand. The analyses also suggest that the capacity has to be expanded gradually near to 24 million passengers in the next five years. In the meantime, the saturation of the Indonesian economy which is reflected by Gross Domestic Product (GDP) provides high risks for further airport expansion. If there is no improvement in the macro economy of Indonesia, the airport capacity bottlenecks seem to be worsening nationwide and particularly in the Juanda Airport.