Comparison of Seasonal ARIMA and Support Vector Machine Forecasting Method for International Arrival in Lombok

Hadyanti Utami MY, Silfiana Lis Setyowati, Khairil Anwar Notodiputro, Yenni Angraini, Laily Nissa Atul Mualifah

Abstract


Seasonal Autoregressive Integrated Moving Average is a statistical model designed to analyze and forecast data with that shows seasonal patterns and trends. Support Vector Machine (SVM) is a machine learning-based technique that can be used to forecast time series data. SVM uses the kernel tricks to overcome non-linearity problems, whereas The SARIMA model is well-suited for data that exhibit seasonal fluctuations that repeat over time. Lombok International Airport is the main gateway to West Nusa Tenggara and has become a symbol of tourism growth in the region. Time series analysis is a very useful tool in determining patterns and forecasting the number of international arrivals at Lombok International Airport within a certain period. This study aims to compare the SARIMA model and SVM which can read non-linear patterns in the number of international arrivals at Lombok International Airport. After obtaining the SARIMA and SVM models, the two models are evaluated using test data based on the smallest RMSE value. The SVM model with a linear kernel trick provides the smallest RMSE when compared to SARIMA with SVM RMSE is 238,655. While the best model in Seasonal ARIMA is SARIMA (3,1,0)(1,0,0)12, the forecasting results show SARIMA is better in the forecasting process for the next 10 months.


Keywords


SARIMA; Support Vector Machine; Forecasting; Lombok International Airport; Seasonal Patterns; International Arrivals

Full Text:

PDF

References


E. Pujiati, D. Yuniarti, and R. Goejantoro, “Peramalan Dengan Menggunakan Metode Double Exponential Smoothing Dari Brown (Studi Kasus : Indeks Harga Konsumen (IHK) Kota Samarinda),” J. Eksponensial, vol. 7, no. 1, 2016. [Online]. Available: http://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/23.

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

V. N. 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, 2023, doi:10.30598/barekengvol17iss4pp2107-2120.

B. G. Prianda and E. Widodo, “Perbandingan Metode Seasonal Arima Dan Extreme Learning Machine Pada Peramalan Jumlah Wisatawan Mancanegara Ke Bali,” BAREKENG J. Ilmu Mat. dan Terap., vol. 15, no. 4, pp. 639–650, 2021, doi: 10.30598/barekengvol15iss4pp639-650.

A. S. Nugroho, A. B. Witarto, and D. Handoko, “Support Vector Machine: Teori dan Aplikasinya dalam Bioinformatika,” in Proceeding of Indonesian Scientific Meeting in Central Japan, Gifu - Japan, 2003.

N. H. Riyantoni, M. F. Bahreisy, I. Hakim, and D. Rolliawati, “Komparasi Support Vector Machine (Svm) Dan Autoregressive Integrated Moving Average (Arima) Pada Peramalan Hujan Di Daerah Albury, Australia,” J. Sist. Inf. dan Inform., vol. 6, no. 1, pp. 59–68, 2023, doi: 10.47080/simika.v6i1.2412.

L. Budiarti, T. Tarno, and B. Warsito, “Analisis Intervensi dan Deteksi Outlier pada Data Wisatawan Domestik (Studi Kasus di Daerah Istimewa Yogyakarta),” J. Gaussian, vol. 2, no. 1, pp. 39–48, 2013, doi:

14710/j.gauss.2.1.39-48.

G. H. Saputra, A. H. Wigena, and B. Sartono, “Penggunaan Support Vector Regression dalam Pemodelan Indeks Saham Syariah Indonesia dengan Algoritme Grid Search,” Indonesian J. Stat. App., vol. 3, no. 2, pp. 148–160, 2019, doi: 10.29244/ijsa.v3i2.172.

W. A. Woodwaard, B. P. Sadler, and S. D. Robertson, Time Series for Data Science: Analysis and Forecasting, 1st ed. New York: CRC Press, 2022, doi: 10.1201/9781003089070.

S. Zahara and S. Sugianto, “Peramalan Data Indeks Harga Konsumen Berbasis Time Series,” J. RESTI (Rekayasa Sist. Teknol. Inf.), vol. 5, pp. 24–30, 2021, doi: 10.29207/resti.v5i1.2562.

G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th Ed. Canada: John Wiley & Sons, 2016.

S. Markidakis, S.C. Wheelwright, and V. E. McGee, Metode dan Aplikasi Peramalan Jilid I, (Terjemahan Ir. Hari Suminto), Edisi Kedua. Jakarta: Binarupa Aksara, 1999.

N. P. P. Pratama and T. Sukmono, “Forecasting the Amount of Blood Storage Using the Support Vector Machine (Svm) Method,” in Procedia of Engineering and Life Science (SENASAINS 5th), Sidoarjo, 2022, doi: 10.21070/pels.v3i0.1359.

L. Bi, O. Tsimhoni, and Y. Liu, “Using the support vector regression

approach to model human performance,” IEEE Trans. Syst. Man, Cybern. Part ASystems Humans, vol. 41, no. 3, pp. 410–417, 2011, doi: 10.1109/TSMCA.2010.2078501.

R. E. Cahyono, J. P. Sugiono, and S. Tjandra, “Analisis Kinerja Metode Support Vector Regression ( SVR ) dalam Memprediksi Indeks Harga Konsumen,” JTIM: J. Teknologi Informasi dan Multimedia, vol. 1, no. 2, pp. 106–116, 2019, doi: 10.35746/jtim.v1i2.22.

D. I. Purnama and O. P. Hendarsin, “Peramalan Jumlah Penumpang Berangkat Melalui Transportasi Udara di Sulawesi Tengah Menggunakan Support Vector Regression (SVR),” Jambura J. Math., vol. 2, no. 2, pp. 49–59, 2020, doi: 10.34312/jjom.v2i2.4458.

S. Annas, Z. Rais, A. Aswi, I. Indrayasaro, and N. Nurfajriani, Implementation of Support Vector Regression (SVR) Analysis in Predicting Gold Prices in Indonesia, vol. 2023, no. Icsmtr. Atlantis Press International BV, 2023. doi: 10.2991/978-94-6463-332-0_12.

M. B. Pamungkas and A. Wibowo, “Aplikasi Metode Arima Box-Jenkins Untuk Meramalkan Kasus DBD Di Provinsi Jawa Timur,” Indones. J. Public Heal., vol. 12, no. 2, pp. 181–194, 2017, doi: 10.20473/ijph.vl13il.2018.181-194.

K. R. A. Muslihin and B. N. Ruchjana, “Model Autoregressive Moving Average (ARMA) untuk Peramalan Tingkat Inflasi di Indonesia,” Limits J. Math. Its Appl., vol. 20, no. 2, p. 209, 2023, doi: 10.12962/limits.v20i2.15098.




DOI: https://doi.org/10.37905/jjom.v6i2.26478



Copyright (c) 2024 Hadyanti Utami MY, Silfiana Lis Setyowati, Khairil Anwar Notodiputro, Yenni Angraini, Laily Nissa Atul Mualifah

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Jambura Journal of Mathematics has been indexed by

>>>More Indexing<<<


Creative Commons License

Jambura Journal of Mathematics (e-ISSN: 2656-1344) by Department of Mathematics Universitas Negeri Gorontalo is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Powered by Public Knowledge Project OJS. 


Editorial Office


Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B. J. Habibie, Moutong, Tilongkabila, Kabupaten Bone Bolango, Gorontalo, Indonesia
Email: info.jjom@ung.ac.id.


 

slot gacor slot gacor hari ini slot gacor 2025 demo slot pg slot gacor slot gacor