Calendar Variation Time Series Analysis for Forecasting the Number of Domestic Passengers of Juanda International Airport Surabaya
DOI:
https://doi.org/10.25077/jmua.15.3.302-318.2026Keywords:
time series, domestic passanger, airport, calendar variationAbstract
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.
Downloads
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
How to Cite
Issue
Section
License
Copyright (c) 2026 Departemen Matematika dan Sains Data FMIPA UNAND
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All articles published in Jurnal Matematika UNAND (JMUA) are open access and licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). This license allows published content to be freely accessed, shared, redistributed, and adapted for any purpose, including commercial use, provided that the applicable license conditions are followed.
Copyright Holder
The copyright of articles published in Jurnal Matematika UNAND is held by the Departemen Matematika dan Sains Data, Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA), Universitas Andalas (UNAND), in accordance with the journal's stated copyright policy.
Author Rights
Under the CC BY-SA 4.0 license, authors may share, distribute, and reuse their published articles, subject to the applicable license conditions. These rights include:
-
Self-Archiving: Authors may deposit their published articles in institutional repositories, personal websites, or academic platforms.
-
Reuse: Authors may reuse their published work in subsequent publications, research, teaching materials, or other lawful purposes, provided that appropriate attribution is maintained.
-
Distribution: Authors may share and distribute their published articles without requiring additional permission under the terms of the license.
Creative Commons License (CC BY-SA 4.0)
Under the CC BY-SA 4.0 license, users are free to:
-
Share: Copy and redistribute the material in any medium or format.
-
Adapt: Remix, transform, and build upon the material for any purpose, including commercial purposes.
These permissions are subject to the following conditions:
-
Attribution: Users must give appropriate credit to the original author(s), retain relevant copyright notices, provide a link to the license, and indicate if changes were made. Attribution must not imply endorsement by the author(s), copyright holder, or journal.
-
ShareAlike: If users remix, transform, or build upon the material, they must distribute their contributions under the same CC BY-SA 4.0 license or a compatible license.
-
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from exercising the rights granted by the license.
For more information about the CC BY-SA 4.0 license, please visit:
https://creativecommons.org/licenses/by-sa/4.0/
Third-Party Content
Third-party materials, including figures, tables, photographs, and illustrations, are covered by the article's CC BY-SA 4.0 license only when their inclusion under that license is authorized.
Materials excluded from the license must be clearly identified with appropriate copyright notices and attribution. Authors are responsible for obtaining any necessary permissions, while users must comply with the applicable licensing terms or permissions when reusing excluded materials.
Copyright and Licensing Information
Copyright and licensing information should be clearly displayed on each published article's landing page and included in the full-text versions (HTML and PDF), where applicable, to ensure transparency regarding copyright ownership and permitted reuse.
