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Kereta api saat ini telah menjadi salah satu transportasi umum yang populer untuk perjalanan jarak menengah dan jauh. Jumlah penumpang KA sulit diprediksi saat musim liburan. Penelitian ini bertujuan untuk memprediksi jumlah penumpang kereta api menggunakan metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Tahapan yang digunakan dalam penelitian ini meliputi (1) persiapan dataset, (2) data preprocessing, dan (3) pengujian dan metode eksperimen. Model SARIMA yang diperoleh adalah ARIMA(2,1,0)(0,1,2)[12] dengan nilai AIC sebesar 2379,265. Sebuah model diagnostik dilakukan, dan ditemukan bahwa model tersebut cukup baik. Sehingga metode SARIMA yang digunakan dalam memprediksi penumpang adalah akurat.

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Artificial Neural Network Method Dataset Java Island SARIMA Model Train passenger

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Cara Mengutip
Muthoharoh, L., Dimas Wahyu Saputro, Dhea Sukma Agustiana, Fadia Dilla Sabine, Lis Nuraini, Rekzi P. Manullang, Taj Shavira, & Mika Alvionita. (2023). Predicting the Number of Train Passengers in Java Island using SARIMA Model. EKSAKTA: Journal of Sciences and Data Analysis, 4(2), 40–48. https://doi.org/10.20885/EKSAKTA.vol4.iss2.art5
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Referensi

  1. 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.
  2. 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).
  3. 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.
  4. Y. I. Katabba, “Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) untuk Memprediksi Jumlah Penumpang Kereta Api di Pulau Sumatera,” Jambi University, 2021.
  5. 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/
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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).
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
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