EID AL-FITR INFLUENCES THE NUMBER OF TRAIN PASSENGERS ON THE SUMATRA ISLAND (CALENDAR VARIATIONS TIME SERIES MODEL)

  • Muhammad Sjahid Akbar Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia https://orcid.org/0000-0003-1490-4978
  • Dinda Ayu Safira Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
  • Rahmi Fadhilah Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
  • Salma Damayanti Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
  • Riskianto Riskianto Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
  • Khem Puthy Department of Mathematics, Faculty of Science, Royal University of Phnom Penh Phnom Penh, Cambodia
Keywords: ARIMAX, Eid Al-Fitr, Regression, Train

Abstract

The Train is one of the transportation options for land travel on the island of Sumatra because of its affordable cost, comfort, and fast mobility. The majority of the population of Sumatra Island who are Muslims influenced the sharp increase in the number of train passengers during Eid Al-Fitr due to the large number of residents who returned to their hometowns (Sumatra Island). The time of Eid Al-Fitr will change every year on the Gregorian calendar, but it is always the same if using the Hijri calendar. This study aims to predict the number of train passengers on the island of Sumatra based on calendar variations (Eid Al-Fitr). To overcome this calendar variation, time series modeling will be used with the addition of exogenous variables (ARIMAX). This model consists of a time series regression equation added with a time series model of the residual regression equation of an exogenous variable. The resulting model can forecast the number of train passengers in the next few months and find that Eid Al-Fitr affects the data. Every Eid Al-Fitr, there is an increase in the number of train passengers by 49 passengers. The model obtained is in the good category with MAPE in-sample of 18.12% and out-sample of 9.93%.

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References

H. A. Karim. S. H Lis Lesmini. D. A Sunarta... & M. Bus. Manajemen transportasi. Surabaya: Cendikia Mulia Mandiri. 2023.

R. Ravico. & B Susetyo. “Sejarah Pembangunan Jalur Kereta Api Sebagai Alat Transportasi Di Sumatera Selatan Tahun 1914-1933.” Agastya J. Sej. Dan Pembelajarannya. vol. 11. no. 1. pp. 68–82. 2021.

I. S. Nurjanah. D. Ruhiat. and D. Andiani. “Implementasi Model Autoregressive Integrated Moving Average (Arima) Untuk Peramalan Jumlah Penumpang Kereta Api Di Pulau Sumatera.” TEOREMA Teor. dan Ris. Mat.. vol. 3. no. 2. p. 145. 2018. doi: 10.25157/teorema.v3i2.1421.

N. N. D. Hayati and S. Martha. “Prediksi Data Jumlah Penumpang Kereta Dengan Efek Variasi Kalender Pada Model Sarimax.” Bimaster Bul. Ilm. Mat. Stat. …. vol. 10. no. 4. pp. 379–388. 2021. [Online]. Available: https://jurnal.untan.ac.id/index.php/jbmstr/article/view/49536%0Ahttps://jurnal.untan.ac.id/index.php/jbmstr/article/download/49536/75676590652

A. R. Nisa. T. Tarno. and A. Rusgiyono. “Peramalan Harga Cabai Merah Menggunakan Model Variasi Kalender Regarima Dengan Moving Holiday Effect (Studi Kasus: Harga Cabai Merah Periode Januari 2012 Sampai Dengan Desember 2019 Di Provinsi Jawa Barat).” J. Gaussian. vol. 9. no. 2. pp. 170–181. 2020. doi: 10.14710/j.gauss.v9i2.27819.

S. N. Intan. E. Zukhronah. and S. Wibowo. “Peramalan Banyaknya Pengunjung Pantai Glagah Menggunakan Metode Autoregressive Integrated Moving Average Exogenous (ARIMAX) dengan Efek Variasi Kalender.” Indones. J. Appl. Stat.. vol. 1. no. 2. p. 70. 2019. doi: 10.13057/ijas.v1i2.26298.

Indonesia. Government Regulation Number 33 of 2021 on The Organization of The Railway Sector. Law Number. indonesia. 2021.

W. W. S. Wei. Time Series Analysis Univariate and Multivariate Methods. 2nd ed. New York: Pearson Education. 2006.

R. J. Makridakis. S.. Wheelwright. S. C.. dan Hyndman. Forecasting Methods and Applications. New Jersey: John Wiley & Sons. 2008.

J. E. Hanke and D. Wichern. Business Forecasting. 9th ed. New Jersey: Pearson Education. 2014.

J. D. Cryer and K.-S. Chan. “Time Series Analysis - Front Pages.” Time Time Ser. Anal. with Appl. R. 2008.

G. T. Wilson. “Time Series Analysis: Forecasting and Control. 5th Edition. by George E. P. Box. Gwilym M. Jenkins. Gregory C. Reinsel and Greta M. Ljung. 2015. Published by John Wiley and Sons Inc.. Hoboken. New Jersey. pp. 712. ISBN: 978‐1‐118‐67502‐1.” J. Time Ser. Anal.. vol. 37. no. 5. pp. 709–711. 2016. doi: 10.1111/jtsa.12194.

D. Rosadi. Analisis Ekonometrika dan Runtun Waktu Terapan dengan R. Yogyakarta: Andi. 2011.

M. Cools. E. Moons. and G. Wets. “Investigating the variability in daily traffic counts through use of ARIMAX and SARIMAX models.” Transp. Res. Rec.. no. 2136. pp. 57–66. 2009. doi: 10.3141/2136-07.

W. W. Daniel. Statistika Nonparametrik Terapan. Jakarta: PT. Gramedia. 1989.

A. Al-Khowarizmi. O. S. Sitompul. S. Suherman. and E. B. Nababan. “Measuring the Accuracy of Simple Evolving Connectionist System with Varying Distance Formulas.” J. Phys. Conf. Ser.. vol. 930. no. 1. 2017. doi: 10.1088/1742-6596/930/1/012004.

BPS. “Jumlah Penumpang Kereta Api (ribu orang). 2006-2023.” September 2023. 2023. Jumlah Penumpang Kereta Api - Tabel Statistik - Badan Pusat Statistik Indonesia (bps.go.id) (accessed :Nov. 15. 2023).

M S Akbar et al. GSTAR-SUR Modeling With Calendar Variations And Intervention To Forecast Outflow Of Currencies In Java Indonesia. Journal of Physics.: Conference. Series. 974 012060. 2018. doi :10.1088/1742-6596/974/1/012060.

Published
2024-10-11
How to Cite
[1]
M. Akbar, D. Safira, R. Fadhilah, S. Damayanti, R. Riskianto, and K. Puthy, “EID AL-FITR INFLUENCES THE NUMBER OF TRAIN PASSENGERS ON THE SUMATRA ISLAND (CALENDAR VARIATIONS TIME SERIES MODEL)”, BAREKENG: J. Math. & App., vol. 18, no. 4, pp. 2191-2202, Oct. 2024.