Forecasting Number of Train Passengers Using Time Series Regression Integrated Calendar Variation and Covid 19 Intervention
Keywords: calendar variation, COVID 19 intervention, time series regression
Abstract
The purpose of this study is to obtain a forecasting model for the number of train passengers using time series regression integrated with variations in the Islamic calendar and the effects of COVID 19. This study uses the number of train passengers in Jabodetabek, Java (Non-Jabodetabek), and Sumatra from January 2006 to December 2022 as the data source. Time series regression with variations of the Islamic calendar and the effects of COVID 19 for Jabodetabek, Java (non-Jabodetabek), and Sumatra has an RMSE value for each category of 7657,821; 2453.827 and 275.901. In general, the number of train passengers for all categories (Jabodetabek, Java, Sumatra) has a seasonality. In Jabodetabek and Sumatra, Eid al-Fitr has a big impact on the number of train passengers. Meanwhile, one month before Eid al-Fitr has a big impact on the number of train passengers in Java (Non Jabodetabek). In addition, the impact of COVID 19 significantly affected the number of train passengers for all categories.
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References
Andalita, I. & Irhamah. (2015). Peramalan Jumlah Penumpang Kereta Api Kelas Ekonomi Kertajaya Menggunakan ARIMA dan ANFIS. Jurnal Sains dan Seni ITS, 4(2), doi:10.12962/j23373520.v4i2.11624.
Anggraeni, W., Vinarti, R. A. & Kurniawati, Y.D. (2015). Performance Comparisons Between Arima and Arimax Method in Moslem Kids Clothes Demand Forecasting: Case Study. Procedia Computer Science, 72, 630-637. https://doi.org/10.1016/j.procs.2015.12.172
Fatimah, S. (2019). Pengantar Transportasi. Ponorogo: Myria Publisher.
Hyndman, R. J. & G. Athanasopoulos. (2018). Forecasting: Principles and Practice 2nd ed. Australia: Monash University.
Ismail, Z., Suhartono, Yahaya, A. & Efendi, R. (2009). Intervention Model for Analyzing the Impact of Terrorism to Tourism Industry. Journal of Mathematics and Statistics, 5(4), 322-329. https://doi.org/10.3844/jmssp.2009.322.329
Nurjanah, I. S., Ruhiat, D., Andiani, D. (2018). Implementasi Model Autoregressive Integrated Moving Average (ARIMA) untuk Peramalan Jumlah Penumpang Kereta Api di Pulau Sumatera. Teorema: Teori dan Riset Matematika, 3(2), 145-156. http://dx.doi.org/10.25157/teorema.v3i2.1421
Rianda, F. (2021). Pemodelan Intervensi Untuk Menganalisis dan Meramalkan Jumlah Penumpang Pesawat di Bandara Soekarno-Hatta Akibat Pandemi Covid-19. Prosiding Seminar Nasional Official Statistics, 283-292. https://doi.org/10.34123/semnasoffstat.v2021i1.857
Silfiani, M., Hayati, F.N., Sari, S.P., & Prabowo, A. (2022). COVID-19 Intervention Model for Bali Province GRDP Prediction. Eigen Mathematics Journal, 5(2), 86-91. https://doi.org/10.29303/emj.v5i2.141
Silfiani, M., Hayati, F.N., Nurlaily, D., & Fitria, I. (2021). Household Electrical Load Forecasting: a Hybrid of Linear Models and Radial Basis Function Neural Network. International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), 253-257. https://doi.org/10.1109/ICAMIMIA54022.2021.9807693
Suhartono, Lee, M. H., & Prasetyo, D. (2015). Two Levels ARIMAX and Regression Models for Forecasting Time Series Data with Calendar Variation Effects. AIP Conference Proceedings 1691, 050026. https://doi.org/10.1063/1.4937108
Susanti, A., Suhartono & Setyadi., H. J., Taruk, M., Hawiludin, Widagdo, P.P. (2018). Forecasting Inflow and Outflow of Money Currency in East Java Using a Hybrid Exponential Smoothing and Calendar Variation Model. Journal of Physics: Conf. Series 979, 012096. https://doi.org/10.1088/1742-6596/979/1/012096
Utomo, P. & Fanani, A. (2020). Peramalan Jumlah Penumpang Kereta Api di Indonesia Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Jurnal Algebra, 1(2), 169–178. https://doi.org/10.29080/algebra.v1i2.6.
Wei, W. (1990). Time Series Analysis: Univariate and Multivariate Methods. New York: Addison-Wesley Publishing Company, Inc.
Wijiyanto, A. N., Kusrini, D. E., & Irhamah. (2012). Peramalan Nilai Kontrak Konstruksi PT ‘X’ dengan Menggunakan Pendekatan Regresi time series dan ANFIS. Jurnal Saing dan Seni ITK. 1(1), D201-D206.
Wulansari, R. E. Suryanto, E., Ferawati, K., Andalita, I., & Suhartono. (2014). Uang Kartal Bank Indonesia Sebagai Solusi Kontrol Likuiditas Perbankan di Indonesia. Statistika, 14(2), 59-68. https://doi.org/10.29313/jstat.v14i2.1203
Zukhronah, E., Sulandari, W., Slamet, I., Sugiyanto & Susanto, I. (2021). Model Variasi Kalender pada Regresi time seriesuntuk Peramalan Jumlah Pengunjung Grojokan Sewu. Indonesian Journal of Applied Statistics, 4(2), 67-75.
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