Evaluation of the SARIMA and Prophet Models in Forecasting Ship Passenger Numbers at Balikpapan Port

Meavi Cintani; Yeky Abil Nizar; Yenni Angraini; Khairil Anwar Notodiputro; Laily Nissa Atul Mualifah

doi:10.37905/jjom.v7i2.31606

Evaluation of the SARIMA and Prophet Models in Forecasting Ship Passenger Numbers at Balikpapan Port

Meavi Cintani, Yeky Abil Nizar, Yenni Angraini, Khairil Anwar Notodiputro, Laily Nissa Atul Mualifah

Abstract

Balikpapan Port serves as a vital transportation hub in eastern Indonesia, particularly in supporting the development of the Nusantara Capital City (IKN). This study evaluates the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet models in predicting short-term ship passenger volumes using monthly data from January 2006 to December 2024 obtained from the East Kalimantan Provincial Transportation Office. Our analysis identifies SARIMA (MAPE = 24%) as the more accurate model compared to Prophet (MAPE = 34%). The optimal SARIMA model was then used to generate a focused forecast for December 2025, providing targeted insights for peak-season port management. These results assist port authorities in resource allocation, infrastructure planning, and policy formulation to accommodate anticipated passenger surges during critical periods.

Keywords

Forecasting; Time series; SARIMA; Prophet; Balikpapan Port

Full Text:

PDF

References

N. Muammar and A. Mosyofa, “Kebijakan Maritim Indonesia Dalam Menunjang Sistem Keamanan Transportasi Laut,” Ris. Sains dan Teknol. Kelaut., vol. 7, no. 1, pp. 46–50, 2024, doi: 10.62012/sensistek.v7i1.31639.

N. S. Khoiriyah, M. Silfiani, R. Novelinda, and S. M. Rezki, “Peramalan Jumlah Penumpang Kapal di Pelabuhan Balikpapan dengan SARIMA,” J. Stat. dan Komputasi, vol. 2, no. 2, pp. 76–82, 2023, doi: 10.32665/statkom.v2i2.2303.

D. Delmas et al., Transport, stability, and biological activity of resveratrol, vol. 1215, no. 1. 2011. doi: 10.1111/j.1749-6632.2010.05871.x.

Badan Perencanaan Pembangunan Nasional, “Handbook of the Relocation of Indonesia’s Capital City,” Kementrian PPN/Bappenas, pp. 1–29, 2021.

G. A. Rob J Hyndman, Forecasting: Principles and Practice. Melbourne: OTexts, 2021.

V. P. Ariyanti and Tristyanti Yusnitasari, “Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 2, pp. 405–413, 2023, doi: 10.29207/resti.v7i2.4895.

S. J. Taylor and B. Letham, “Forecasting at Scale,” Am. Stat., vol. 72, no. 1, pp. 37–45, Jan. 2018, doi: 10.1080/00031305.2017.1380080.

R. I. P. Negara, “Peramalan Jumlah Penumpang Kapal di Pelabuhan Pantai Baru dengan Metode Sarima Dan Winter ’ s Exponential Smoothing,” Jstar, vol. 1, no. 1, pp. 63–78, 2021.

V. Nur Aziza, F. H. Moh’d, F. A. Maghfiroh, K. A. Notodiputro, and Y. Angraini, “Performance Comparison of Sarima Intervention and Prophet Models for Forecasting the Number of Airline Passenger At Soekarno-Hatta International Airport,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 4, pp. 2107–2120, Dec. 2023, doi: 10.30598/barekengvol17iss4pp2107-2120.

A. Ubaid, F. Hussain, and M. Saqib, “Container shipment demand forecasting in the Australian shipping industry: A case study of asia–oceania trade lane,” J. Mar. Sci. Eng., vol. 9, no. 9, p. 968, Sep. 2021, doi: 10.3390/jmse9090968.

S. Bouhaddour, C. Saadi, I. Bouabdallaoui, F. Guerouate, and M. Sbihi, “Tourism in Singapore, prediction model using SARIMA and PROPHET,” in AIP Conference Proceedings, 2023, p. 020042. doi: 10.1063/5.0131288.

Badan Pusat Statistik, “Total Penumpang Pelayaran dalam Negeri di Pelabuhan Utama (Orang),” Https://Bps.Go.Id/. Accessed: Feb. 10, 2025. [Online]. Available: https://www.bps.go.id/id/statistics-table/2/NjkjMg==/total-penumpang-pelayaran-dalam-negeri-di-pelabuhan-utama--orang-.html

Mursidah, Yunina, Nurhasanah, and D. Yuni, “Perbandingan Metode Exponential Smoothing Dan Metode Decomposition Untuk Meramalkan Persediaan Beras (Studi Kasus Divre Bulog Lhokseumawe ),” J. Visioner Strateg., vol. 10, no. 1, pp. 37–46, 2021.

M. Geurts, G. E. P. Box, and G. M. Jenkins, Time Series Analysis: Forecasting and Control, vol. 14, no. 2. Hoboken, NJ, USA: John Wiley & Sons, 1977. doi: 10.2307/3150485.

S. Makridakis and dkk, Metode dan Aplikasi Peramalan. Jilid I (Edisi Kedua), Terjemahan Ir. Hari Suminto, Edisi Kedu. Jakarta: Binarupa Aksara, 1999.

M. B. Pamungkas, “Aplikasi Metode Arima Box-Jenkins Untuk Meramalkan Kasus Dbd Di Provinsi Jawa Timur,” Indones. J. Public Heal., vol. 13, no. 2, p. 183, Feb. 2019, doi: 10.20473/ijph.v13i2.2018.183-196.

B. A. Hakim, Billy, K. A. Notodiputro, Y. Angraini, and L. N. A. Mualifah, “Time Series Model for Train Passenger Forecasting,” Barekeng, vol. 19, no. 2, pp. 755–766, Apr. 2025, doi: 10.30598/barekengvol19iss2pp755-766.

Y. L. Zhang, Y. T. Bai, X. B. Jin, T. L. Su, J. L. Kong, and W. Z. Zheng, “Deep Fusion Prediction Method for Nonstationary Time Series Based on Feature Augmentation and Extraction,” Appl. Sci., vol. 13, no. 8, p. 5088, Apr. 2023, doi: 10.3390/app13085088.

H. U. MY, S. L. Setyowati, K. A. Notodiputro, Y. Angraini, and L. N. A. Mualifah, “Comparison of Seasonal ARIMA and Support Vector Machine Forecasting Method for International Arrival in Lombok,” Jambura J. Math., vol. 6, no. 2, pp. 212–219, 2024, doi: 10.37905/jjom.v6i2.26478.

F. E. Stiftung, “Economic Impact of the Global Financial Crisis on Indonesia: A Regional Perspective,” Friedrich Ebert Stiftung, 2009.

Z. Luo et al., “A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China,” Int. J. Environ. Res. Public Health, vol. 19, no. 10, p. 5910, May 2022, doi: 10.3390/ijerph19105910.

DOI: https://doi.org/10.37905/jjom.v7i2.31606