PERAMALAN JUMLAH PENUMPANG PESAWAT DI BANDAR UDARA INTERNASIONAL JUANDA MENGGUNAKAN METODE EXPONENTIAL SMOOTHING EVENT-BASED
Abstract
The increase in the number of airplane passengers occurs at certain times, such as Eid al-Adha, Eid al-Fitr, and Christmas holidays. Of course, an excessive rise in the number of passengers can cause extreme flight traffic density so that which can cause flight delays, decreased airport service level performance, and other impacts. This study predicts the number of aircraft passengers at Juanda International Airport using the Exponential Smoothing Event-Based method. The Exponential Smoothing Event-Based method is a forecasting method that considers special events using the Exponential Smoothing method as the initial calculation. This study uses data on the number of passengers from January 2014 to December 2020. From the forecasting model, MAPE is 11.8905%, and MSE is 4202958561.0706, so that the resulting forecast can be categorized as good.
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References
Rahmat Gazali, “ini Alasan Jasa Angkutan Udara Lebih Diminati daripada Jasa Angkutan Laut,” borneonews.co.id, 2018. https://www.borneonews.co.id/berita/108431-ini-alasan-jasa-angkutan-udara-lebih-diminati-daripada-jasa-angkutan-laut (accessed Jan. 28, 2021).
A. Darmanto, “Faktor yang Mempengaruhi Permintaan Jasa Transportasi Penyeberangan antar Pulau di Kota Raha,” 2014.
D. Kurnia, “7 Bandara Tersibuk di Indonesia, Sudah Pernah Mendarat di Sini?,” Tiket.com, 2020. https://blog.tiket.com/bandara-tersibuk-di-indonesia/ (accessed Mar. 15, 2021).
S. Yuliardi, “Tiga Bandara Indonesia Raih Penghargaan Dunia,” wartaekonomi.co.id, 2018. TIGA BANDARA ANGKASA PURA I RAIH PENGHARGAAN PRESTISIUS DUNIA ASQ AWARDS 2017, BANDARA I GUSTI NGURAH RAI BALI RAIH PREDIKAT “BEST AIRPORT 2017” (accessed Jan. 28, 2021).
R. Withycombe, “Forecasting with combined seasonal indices,” Int. J. Forecast., vol. 5, no. 4, pp. 547–552, 1989, doi: 10.1016/0169-2070(89)90010-1.
A. G. Salman and B. Kanigoro, “Visibility Forecasting Using Autoregressive Integrated Moving Average (ARIMA) Models,” Procedia Comput. Sci., vol. 179, no. 2019, pp. 252–259, 2021, doi: 10.1016/j.procs.2021.01.004.
D. K. Barrow, “Forecasting intraday call arrivals using the seasonal moving average method,” J. Bus. Res., vol. 69, no. 12, pp. 6088–6096, 2016, doi: 10.1016/j.jbusres.2016.06.016.
I. N. Putra, I. N. Pujawan, and N. I. Arvitrida, “Peramalan Permintaan Dan Perencanaan Produksi Dengan Mempertimbangkan Special Event Di Pt . Coca-Cola Bottling Indonesia ( Pt . Ccbi ) Plant-Pandaan,” peramalan permintaan dan Perenc. produksi dengan mempertimbangkan Spec. event di PT.COCA-COLA BOTTLING Indones. PLANT-PANDAAN, pp. 1–13, 2006.
S. Henifa, “Peramalan Penjualan Avtur dengan Mempertimbangkan Special Event,” 2014.
S. Dheviani and P. Hendikawati, “Peramalan Banyaknya Penumpang Di Bandar Udara Internasional Ahmad Yani Semarang Dengan Mempertimbangkan Special Event,” Prisma, vol. 1, pp. 434–444, 2018.
F. A. Widjajati, “Menentukan Penjualan Produk Terbaik di Perusahaan X Dengan Metode Winter Eksponensial Smoothing dan Metode Event Based,” Limits J. Math. Its Appl., vol. 14, no. 1, p. 25, 2017, doi: 10.12962/limits.v14i1.2127.
M. R. F. Payu and N. Nurwan, “Metode Exponential Smoothing Event Based (Eseb) Dan Metode Winter’S Exponential Smoothing (Wes) Untuk Peramalan Jumlah Penumpang Tiba Di Pelabuhan Penyeberangan Gorontalo,” BAREKENG J. Ilmu Mat. dan Terap., vol. 13, no. 3, pp. 197–202, 2019, doi: 10.30598/barekengvol13iss3pp197-202ar935.
Sudjana, Metode Statistika. Bandung: Tarsito, 1986.
Makridakis, S., Wheelwright, S. C., Hyndman, R. J., Forecasting: Methods and Applications. New York: John Wiley, 1998.
P. M. Maçaira, R. C. Souza, and F. L. Cyrino Oliveira, “Modelling and forecasting the residential electricity consumption in Brazil with pegels exponential smoothing techniques,” Procedia Comput. Sci., vol. 55, no. Itqm, pp. 328–335, 2015, doi: 10.1016/j.procs.2015.07.057.
R. Biri, Y. A. . Langi, and M. S. Paendong, “Penggunaan Metode Smoothing Eksponensial Dalam Meramal Pergerakan Inflasi Kota Palu,” J. Ilm. Sains, vol. 13, no. 1, p. 68, 2013, doi: 10.35799/jis.13.1.2013.2035.
Aden, Forecasting The Eksponetial Smoothing Methods, no. 1. 2020.
Taylor W. James, “Exponential smoothing with a damped multiplicative trend,” Int. J. Forecast., vol. 19, no. 4, pp. 715–725, 2003.
I. Falani, “Penentuan Nilai Parameter Metode Exponential Smoothing Dengan Algoritma Genetik Dalam Meningkatkan Akurasi Forecasting,” Comput. Eng. Sci. Syst. J., vol. 3, no. 1, p. 14, 2018, doi: 10.24114/cess.v3i1.8268.
D. W. Bunn and A. I. Vassilopoulos, “Using group seasonal indices in multi-item short-term forecasting,” Int. J. Forecast., vol. 9, no. 4, pp. 517–526, 1993, doi: 10.1016/0169-2070(93)90078-2.
J. S. Armstrong and F. Collopy, “Error measures for generalizing about forecasting methods: Empirical comparisons,” Int. J. Forecast., vol. 8, no. 1, pp. 69–80, 1992, doi: 10.1016/0169-2070(92)90008-W.
D. Andaka, “DAMPAK PELARANGAN MUDIK AKIBAT PANDEMI COVID19 dan sebagian kawasan lainnya masih menunjukkan peningkatan yang signifikan . peningkatan Kementerian Perhubungan dalam bentuk Peraturan Menteri Perhubungan ( Permenhub ) Nomor,” vol. 1, no. 2, pp. 116–129, 2020.
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