Calendar Variation Time Series Analysis for Forecasting the Number of Domestic Passengers of Juanda International Airport Surabaya

Authors

  • Putriaji Hendikawati FMIPA Universitas Negeri Semarng
  • Anisa Oktaviani

DOI:

https://doi.org/10.25077/jmua.15.3.302-318.2026

Keywords:

time series, domestic passanger, airport, calendar variation

Abstract

This study focuses on forecasting the number of domestic transportation passengers at Juanda International Airport using a time series model by including exogenous variables of calendar variation effects of Eid Al-Fitr and Eid Al-Adha holidays. Data of the number of domestic passengers at Juanda International Airport from the Statistics Indonesia (BDS) was analyzed for the period January 2014 to May 2024. The models used include Dummy Regression, Trend Regression, ARIMA, SARIMA, ARIMAX, and SARIMAX. The analysis results show that the ARIMAX model produces the most accurate prediction. Juanda Airport passenger data is optimal in the ARIMAX ([1,11],1,1) model with the exogenous variable Eid al-Fitr dummy which produces MAPE:7.49; RMSE: 41,133.26; and MAE: 34.119,06. The results of the analysis show that including exogenous variables in the form of calendar variations in the forecasting model can improve prediction accuracy.

References

[1] BPS, 2023a, Kota Surabaya Dalam Angka 2023, Bappeda Potensi Wilayah, Vol. 4, Issue 1. https://bappeda.jatimprov.go.id/bappeda/wp-content/ uploads/potensi-kab-kota-2013/kota-surabaya-2013.pdf.

[2] BPS, 2023c, STATISTIK TRANSPORTASI LAUT 2022, Vol. 8. https://repositorio.unan.edu.ni/2986/1/5624.pdf.

[3] BPS, 2023b, STATISTIK INDONESIA 2023, Statistik Indonesia 2023, Vol. 1101001. https://www.bps.go.id/publication/2020/04/29/ e9011b3155d45d70823c141f/statistik-indonesia-2020.html

[4] Hyndman, R.J. & Athanasopoulos, G., 2021, Forecasting: Principles and Practice. 3rd Edition, Otexts Publishing.

[5] Putri, S., & Sofro, A., 2022, Peramalan Jumlah Keberangkatan Penumpang Pelayaran Dalam Negeri di Pelabuhan Tanjung Perak Menggunakan Metode ARIMA dan SARIMA, MATHunesa: Jurnal Ilmiah Matematika, Vol. 10, Is- sue 1, pp. 61–67. https://doi.org/10.26740/mathunesa.v10n1.p61-67.

[6] Putra, E.F., Asdi, Y. & Maiyastri, M., 2019, Peramalan Dengan Metode Pemulusan Eksponensial Holt-Winter Dan SARIMA (Studi Kasus: Jumlah Produksi Ikan (Ton) di Kota Sibolga Tahun 2000-2017), Jurnal Matematika UNAND, 8(1), pp.75-83.

[7] Adnan, R.M., Yuan, X., Kisi, O. & Yuan, Y., 2017, Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average Model, Eur Sci J, 13(12), pp.145-156.

[8] Wang, X. and Liu, Y., 2009, ARIMA Time Series Application to Employ- ment Forecasting, In 2009 4th International Conference on Computer Sci- ence & Education, pp.1124-1127, IEEE.

[9] Wang, Y., Wang, J., Zhao, G. & Dong, Y., 2012, Application of residual modification approach in seasonal ARIMA for electricity demand forecast- ing: A case study of China, Energy Policy, 48, pp.284-294.

[10] Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S. & Ciccozzi, M., 2020, Application of the ARIMA Model On The COVID-2019 Epidemic Dataset, Data in Brief, 29, p.105340.

[11] Berlinditya, B., & Noeryanti, N. , 2019, Pemodelan Time Series Dalam Peramalan Jumlah Pengunjung Objek Wisata Di Kabupaten Gunung Kidul Menggunakan Metode ARIMAX Efek Variasi Kalender, Jurnal Statistika Industri Dan Komputasi, Vol.4, Issue 1, pp. 81–88.

[12] Dani, A. T. R., Wahyuningsih, S., Putra, F. B., Fauziyah, M., Wigantono, S., Sandariria, H., A’yun, Q. Q., & Zen, M. A., 2023, Aplikasi Model ARI- MAX dengan Efek Variasi Kalender untuk Peramalan Trend Pencarian Kata Kunci “Zalora” pada Data Google Trends, Inferensi, Vol.6, Issue 2, pp. 107. https://doi.org/10.12962/j27213862.v6i2.15793.

[13] Farih, L., Fauziah, I., & Wijaya, M., 2019, Prediction of The Number of Ship Passengers in The Port of Makassar using ARIMAX Method in The Presence of Calendar Variation, InPrime: Indonesian Journal of Pure and Applied Mathematics, Vol.1, Issue 1, pp. 57–67. https://doi.org/10.15408/inprime.v1i1.12786

[14] Qomariyah Virati, M., Paulus Pamanik, D., & Pramana, S., 2020, Fore- casting Number of Passengers of TransJakarta using SARIMAX Method, Open Access J Data Sci Appl, Vol.3, Issue 1, pp. 31–37. https://doi.org/ 10.34818/JDSA.2020.3.45

[15] Latief, N. H., Nur’eni, N., & Setiawan, I., 2022, Peramalan Curah Hujan di Kota Makassar dengan Menggunakan Metode SARIMAX, STATISTIKA Journal of Theoretical Statistics and Its Applications, Vol.22, Issue 1, pp. 55–63. https://doi.org/10.29313/statistika.v22i1.990

[16] Kartiningtyas, A. N., Zukhronah, E., & Sugiyanto, 2019, Model ARIMAX untuk Meramalkan Banyak Penumpang dari Pelayaran Dalam Negeri di Pelabuhan Tanjung Priok, Prosiding Sendika, Vol.5, Issue 2, pp. 6–15.

[17] Syaharuddin, Akmala, Q. S., & Sucipto, L., 2022, Metode ARIMA, ARI- MAX, dan SARIMA: Sebuah Meta-Analisis Perbedaan Tingkat Akurasi Peramalan Data Time Series, Jurnal Informatika Kaputama (JIK), Vol.6, Issue 3, pp. 502–509.

[18] Liu, L.M., 1986, Identification of Time Series Models in The Presence of Calendar Variation, International Journal of Forecasting, 2(3), 357-372. [19] Hillmer, S.C., 1982, Forecasting Time Series with Trading Day Variation, Journal of Forecasting, 1(4), 385-395.

[20] Bell, W.R., dan Hillmer, S.C., 1983, Modeling Time Series with Calendar Variation, Journal of the American statistical Association, 78(383), 526-534. [21] Fox, J., 2016, Applied Regression Analyis and Generated Linear Models 3rd Edition, Journal of Chemical Information and Modeling, Vol. 53, Issue

9.

[22] Box, G.E.P., Jenkins, G.M., 2015, Time Series Analysis: Forecasting And Control, 5th Edition, John Wiley and Sons Inc., Hoboken, New Jersey. [23] Suryani, R. V., Rismawan, T., & Ruslianto, I., 2023, Penerapan Metode Arima Untuk Memprediksi Pemakaian Bandwidth Di Universitas Tanjungpura, Coding Jurnal Komputer dan Aplikasi, 10(03), pp.421-432.

[24] Makridakis, S., Wheelwright, S. C., & Hyndman, R. J., 1998, Forecasting: Methods and Applications, 3rd

Edition, Wiley. https://doi.org/10.2307/2287014.

[25] Hendikawati, P., Subanar, S., Abdurakhman, A., & Tarno, T., 2022, Effects of Calendar Variations on the Indonesia Stock Exchange: an Empirical Study of Potential Stocks, Statmat: Jurnal Statistika Dan Matematika, 4(1), 39–60, https://doi.org/10.32493/sm.v4i1.14921

[26] Nasirudin, F., & Dzikrullah, A.A., 2023, Pemodelan Harga Cabai Indone- sia dengan Metode Seasonal ARIMAX, Jurnal Statistika Dan Aplikasinya, 7(1),105–115, https://doi.org/10.21009/jsa.07110

[27] Shuja, N., Lazim, M. A., & Wah, Y. B., 2007, Moving Holiday Effects Adjustment for Malaysian Economic Time Series, Department of Statistics.

Downloads

Published

31-07-2026

Issue

Section

Articles