TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING

  • Bashir Ammar Hakim Department of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, Indonesia https://orcid.org/0009-0000-5844-2635
  • Billy Billy Department of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, Indonesia https://orcid.org/0009-0004-1794-8583
  • Khairil Anwar Notodiputro Department of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, Indonesia https://orcid.org/0000-0003-2892-4689
  • Yenni Angraini Department of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, Indonesia https://orcid.org/0000-0003-3186-2378
  • Laily Nissa Atul Mualifah Department of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, Indonesia https://orcid.org/0000-0002-5722-8431
DOI: https://doi.org/10.30598/barekengvol19iss2pp755-766
Keywords: ARIMA, Passenger, Prophet, Time Series, Train

Abstract

Trains as a means of public transportation have an important role in connecting various regions of Jabodetabek. Therefore, it is necessary to have a deep understanding of the trend of train passenger movements and predict the number of train passengers in the next period in order to optimize the management and service of train passengers properly. In this study, we examine two methods that can be used as forecasting methods for train passenger data sourced from the Central Statistics Agency (BPS), namely ARIMA and Prophet. This study demonstrates that the optimal ARIMA model is ARIMA (0,2,1), achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and a Root Mean Square Error (RMSE) of 1754.970. In addition, the Prophet model, which is an additive regression model designed by Facebook for time series forecasting was also obtained with a MAPE of 0.04% and an RMSE of 1170.59. Considering the MAPE and RMSE values of the two models, the Prophet model emerges as the most suitable for forecasting the number of train passengers in the Jabodetabek region.

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Published
2025-04-01
How to Cite
[1]
B. A. Hakim, B. Billy, K. A. Notodiputro, Y. Angraini, and L. N. A. Mualifah, “TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING”, BAREKENG: J. Math. & App., vol. 19, no. 2, pp. 755-766, Apr. 2025.
Issue
Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2025 Bashir Ammar Hakim, Billy Billy, Khairil Anwar Notodiputro, Yenni Angraini, Laily Nissa Atul Mualifah

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