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Abstract
The train at this time has become one of the most popular public transportation for medium and long-distance travel. The number of train passengers is difficult to predict during the holiday season. This study aimed to predict the number of train passengers using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The stages used in this study include (1) dataset preparation, (2) preprocessing data, and (3) experimental testing and methods. The SARIMA model obtained is ARIMA(2,1,0)(0,1,2)[12] with an AIC value of 2379,265. A diagnostic model was carried out, and it was found that the model is quite good. So the SARIMA method used in predicting passengers is accurate.
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References
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References
P. D. W. Oktama, “Nowcasting Jumlah Penumpang Kereta Api di Indonesia Menggunakan Indeks Google Trends,” Semin. Nas. Off. Stat., vol. 2021, no. 1, pp. 958–967, 2021, doi: 10.34123/semnasoffstat.v2021i1.820.
M. A. Rizaty, “Berapa Jumlah Stasiun Kereta Api Penumpang di Jawa dan Sumatera?,” databooks, 2021. https://databoks.katadata.co.id/datapublish/2021/11/04/berapa-jumlah-stasiun-kereta-api-penumpang-di-jawa-dan-sumatera (accessed May 30, 2022).
Suryadi, “Kinerja Dan Peramalan Pertumbuhan Angkutan Kereta Api Menggunakan Model Sarima,” War. Penelit. Perhub., vol. 26, no. 7, p. 381, 2019, doi: 10.25104/warlit.v26i7.922.
Y. I. Katabba, “Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) untuk Memprediksi Jumlah Penumpang Kereta Api di Pulau Sumatera,” Jambi University, 2021.
B. W. Arianto, “Peramalan Jumlah Penumpang Kereta Api Di Pulau Jawa Dan Sumatera Menggunakan Arima Box-Jenkins,” Institut Teknologi Sepuluh November, 2017. [Online]. Available: http://repository.its.ac.id/43384/
T. Widiyaningtyas, Muladi, and A. Qonita, “Use of ARIMA Method to Predict the Number of Train Passenger in Malang City,” Proceeding - 2019 Int. Conf. Artif. Intell. Inf. Technol. ICAIIT 2019, pp. 359–364, 2019, doi: 10.1109/ICAIIT.2019.8834663.
A. Qonita, A. G. Pertiwi, and T. Widiyaningtyas, “Prediction of rupiah against us dollar by using ARIMA,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2017-Decem, no. September, pp. 19–21, 2017, doi: 10.1109/EECSI.2017.8239205.
C. F. Chen, Y. H. Chang, and Y. W. Chang, “Seasonal ARIMA forecasting of inbound air travel arrivals to Taiwan,” Transportmetrica, vol. 5, no. 2, pp. 125–140, 2009, doi: 10.1080/18128600802591210.
N. AISHAH, D. DEVIANTO, and M. MAIYASTRI, “Pemodelan Jumlah Kunjungan Wisatawan Mancanegara Ke Indonesia Melaui Bandara Ngurah Rai Bali Dengan Model Sarima-Arch,” J. Mat. UNAND, vol. 10, no. 3, p. 248, 2021, doi: 10.25077/jmu.10.3.248-259.2021.
E. Padang, G. Tarigan, and U. Sinulingga, “Peramalan Jumlah Penumpang Kereta Api Medan-Rantau Prapat Dengan Metode Pemulusan Eksponensial Holt-Winters,” Saintia Mat., vol. 1, no. 2, pp. 161–174, 2013.
S. Salmon, N. Nainggolan, and D. Hatidja, “Pemodelan ARIMA Dalam Prediksi Penumpang Pesawat Terbang Pada Bandara Internasional Sam Ratulangi Manado,” d’CARTESIAN, vol. 4, no. 1, p. 59, 2015, doi: 10.35799/dc.4.1.2015.8099.
J. Zhu, W. Xu, H. Jin, and H. Sun, “Prediction of Urban Rail Traf fi c Flow Based on Multiply Wavelet-ARIMA Model Wavelet analysis Threshold processing,” pp. 1–12, 2018, doi: 10.1007/978-981-10-3551-7.
J. He and B. Si, “The application of ARIMA-RBF model in urban rail traffic volume forecast,” Proc. 2nd Int. Conf. Comput. Sci. Electron. Eng. (ICCSEE 2013), vol. 1, no. March 2013, 2013, doi: 10.2991/iccsee.2013.416.
B. M. Williams and L. A. Hoel, “Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results,” J. Transp. Eng., vol. 129, no. 6, pp. 664–672, 2003, doi: 10.1061/(ASCE)0733-947X(2003)129:6(664).
W. Rahmalina, “Pemodelan Seasonal Autoregressive Integrated Moving Average Untuk Memprediksi Jumlah Kasus Covid-19 di Padang,” J. Mat. Integr., vol. 17, no. 1, p. 23, 2021, doi: 10.24198/jmi.v17.n1.32024.23-31.
D. Ruhiat and A. Effendi, “Pengaruh Faktor Musiman Pada Pemodelan Deret Waktu Untuk Peramalan Debit Sungai Dengan Metode Sarima,” Teorema, vol. 2, no. 2, pp. 117–128, 2018, doi: 10.25157/.v2i2.1075.
R. Ayu, R. Gernowo, D. Fisika, F. Sains, U. Diponegoro, and S. E-, “Metode Autoregressive Integrated Movingaverage (Arima) Dan Metode Adaptive Neuro Fuzzy Inference System (Anfis) Dalam Analisis Curah Hujan,” Berk. Fis., vol. 22, no. 1, pp. 41–48, 2019.
M. Al Aradi and N. Hewahi, “Prediction of Stock Price and Direction Using Neural Networks: Datasets Hybrid Modeling Approach,” 2020 Int. Conf. Data Anal. Bus. Ind. W. Towar. a Sustain. Econ. ICDABI 2020, 2020, doi: 10.1109/ICDABI51230.2020.9325697.
E. Erdem and J. Shi, “ARMA based approaches for forecasting the tuple of wind speed and direction,” Appl. Energy, vol. 88, no. 4, pp. 1405–1414, 2011, doi: 10.1016/j.apenergy.2010.10.031.