Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

Forecasting of Revenue, Number of Plane Movements and Number of Passenger Movements at Sultan Iskandar Muda International Airport Using the VARIMA Method

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290496,
        author={Asep  Rusyana and Lia  Rahmati and Nurhasanah  Nurhasanah},
        title={Forecasting of Revenue, Number of Plane Movements and Number of Passenger Movements at Sultan Iskandar Muda International Airport Using the VARIMA Method},
        proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia},
        publisher={EAI},
        proceedings_a={ICSA},
        year={2020},
        month={1},
        keywords={aircraft movement forecasting passenger movements revenue varima},
        doi={10.4108/eai.2-8-2019.2290496}
    }
    
  • Asep Rusyana
    Lia Rahmati
    Nurhasanah Nurhasanah
    Year: 2020
    Forecasting of Revenue, Number of Plane Movements and Number of Passenger Movements at Sultan Iskandar Muda International Airport Using the VARIMA Method
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290496
Asep Rusyana1,*, Lia Rahmati1, Nurhasanah Nurhasanah1
  • 1: Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Jalan SyechAbdurrauf No.3, Kopelma Darussalam, Banda Aceh, Aceh, Indonesia 23111
*Contact email: asep.rusyana@unsyiah.ac.id

Abstract

Forecasting has become a necessity in various fields. One of the companies that do the forecasting is PT. Angkasa Pura II - Sultan Iskandar Muda (SIM) International Airport. The amount of income, the number of aircraft movements, and the number of passenger movements are some of the interesting things to study from an airport. This kind of problem can be solved by Vector Autoregressive Integrated Moving Average (VARIMA) method. The variables involved in this data analysis can be modeled and forecasted. The analysis shows that the three variables studied tend to have a flat trend, where the amount of income, the number of aircraft movements, and the number of passenger movements has increased over time. The best model obtained is in the form of VARIMA (1,1,0) model. Percentage of forecast errors for income variables, aircraft movements, and passenger movements amounted to 14.028%, 11.003% and 13.330%, respectively.