Kernel selection in support vector regression for forecasting international tourist arrivals: Implications for economic resilience and national defense
DOI:
https://doi.org/10.58524/app.sci.def.v4i2.1496Keywords:
BI Rate, Forecasting, Kernel, SVR, TouristAbstract
Background: International tourist arrivals are an important component of Indonesia's tourism economy and are influenced by temporal patterns and macroeconomic conditions. Accurate forecasting is therefore important to support tourism planning and economic resilience.
Aims: This study aims to develop and evaluate a Support Vector Regression (SVR) model with different kernel functions for forecasting international tourist arrivals in Indonesia and to compare its performance with the ARIMAX model.
Method: Monthly tourist arrival data from January 2017 to November 2024 were analyzed using SVR with linear, polynomial, radial basis function, and sigmoid kernels. Data were standardized using StandardScaler, while Grid Search was used to optimize the model parameters. Inflation and the BI Rate were incorporated as explanatory variables. The best-performing SVR model was compared with ARIMAX(1,1,1) using MSE, RMSE, MAD, and MAPE.
Result: The SVR with a second-degree Polynomial Kernel achieved the best performance, with an MSE of 3,401,140,779.52, RMSE of 58,319.30, MAD of 44,565.19, and MAPE of 12.94%. The model outperformed ARIMAX(1,1,1), which obtained a MAPE of 16.23%. The results indicate that the polynomial kernel can better capture nonlinear patterns in tourist arrival dynamics.
Conclusion: The SVR Polynomial model provides a useful approach for forecasting international tourist arrivals in Indonesia. Its forecasting capability can support tourism planning, resource allocation, and early identification of changes in tourism demand, contributing to more adaptive and resilient tourism management.
References
Adam, M., Chrisnanto, Y. H., & Umbara, F. R. (2024). Prediksi Pola Keuangan pada Pasar Saham Bursa Efek Indonesia Menggunakan Algoritma Support Vector Machine For Regression (SVR). Jurnal Edukasi & Penelitian Informatika, 10(3), 476–484.
Agan Celik, B., Yulek, M., & Celik, S. (2026). Machine learning assessment of drivers of tourist arrivals in Istanbul: evidence from economic, climatic and behavioral indicators. Journal of Hospitality and Tourism Technology, 1–25. https://doi.org/10.1108/JHTT-08-2025-0686
Akanbi, O. B., & Bello, A. O. (2024). Fitting Autoregressive Integrated Moving Average with Exogenous Variables Model with Lognormal Error Term. Journal of Scientific Research and Reports, 30(10), 158–168. https://doi.org/10.9734/jsrr/2024/v30i102442
Amelia, R., Dalimunthe, D. Y., Kustiawan, E., & Sulistiana, I. (2021). ARIMAX model for rainfall forecasting in Pangkalpinang, Indonesia. IOP Conference Series: Earth and Environmental Science, 926(1), 012034. https://doi.org/10.1088/1755-1315/926/1/012034
Cankurt, S., & Subaşi, A. (2016). Tourism demand modelling and forecasting using data mining techniques in multivariate time series: a case study in Turkey. Turkish Journal Of Electrical Engineering & Computer Sciences, 24, 3388–3404. https://doi.org/10.3906/elk-1311-134
Chen, K.-Y., & Wang, C.-H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 28(1), 215–226. https://doi.org/10.1016/j.tourman.2005.12.018
Chen, Y.-P., Liu, L.-F., Che, Y., Huang, J., Li, G.-X., Sang, G.-X., Xuan, Z.-Q., & He, T.-F. (2022). Modeling and Predicting Pulmonary Tuberculosis Incidence and Its Association with Air Pollution and Meteorological Factors Using an ARIMAX Model: An Ecological Study in Ningbo of China. International Journal of Environmental Research and Public Health, 19(9), 5385. https://doi.org/10.3390/ijerph19095385
Feng, S., Yu, Z., Ren, X., & Yu, S. (2022). The Impact of the COVID-19 Pandemic on the Tourism Industry and What Kind of Future this Industry will be Led to. https://doi.org/10.2991/aebmr.k.220405.317
Guerouate, F., Sbihi, M., & Laaroussi, H. (2023). Deep learning approach and topic modelling for forecasting tourist arrivals. International Journal of Electrical and Computer Engineering Systems, 14(4), 411–419. https://doi.org/10.32985/ijeces.14.4.5
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.) (3rd editiom). OTexts.
Iriananda, S. W., Budiawan, R. W., Rahman, A. Y., & Istiadi, I. (2024). Optimasi Klasifikasi Sentimen Komentar Pengguna Game Bergerak Menggunakan Svm, Grid Search Dan Kombinasi N-Gram. Jurnal Teknologi Informasi Dan Ilmu Komputer, 11(4), 743–752. https://doi.org/10.25126/jtiik.1148244
Jouilil, Y., & Mentagui, D. (2023). Comparing The Forecasting Accuracy Metrics of Support Vector Regression and ARIMA Algorithms for Non-Stationary Time Process. Mathematics and Statistics, 11(2), 294–299. https://doi.org/10.13189/ms.2023.110207
Lalian, V. U., Bekti, R. D., Pratiwi, N., Sutanta, E., & Pradnyana, I. W. J. (2022). Analysis of Consumer Satisfaction Levels with GoRide Services Using the Support Vector Machine (SVM) Classification Method. 2022 IEEE 8th Information Technology International Seminar (ITIS), 51–56. https://doi.org/10.1109/ITIS57155.2022.10010286
Maulana, N. D., Setiawan, B. D., & Dewi, C. (2019). Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus: Harum Bakery). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(3), 2986–2995.
Mishra, R. K., Urolagin, S., Jothi, J. A. A., Nawaz, N., & Ramkissoon, H. (2021). Machine Learning based Forecasting Systems for Worldwide International Tourists Arrival. International Journal of Advanced Computer Science and Applications, 12(11). https://doi.org/10.14569/IJACSA.2021.0121107
Nguyen, T.-L., Shu, M.-H., Huang, Y.-F., & Hsu, B.-M. (2013). Accurate forecasting models in predicting the inbound tourism demand in Vietnam. Journal of Statistics and Management Systems, 16(1), 25–43. https://doi.org/10.1080/09720510.2013.777570
Nur Aini, L. (2022). Pengaruh Inflasi, Bank Indonesia Rate Dan Nilai Tukar Rupiah Terhadap Harga Saham Pada Perusahaan Sektor Transportasi Dan Logistik Di Bursa Efek Indonesia Periode 2015-2018. SIBATIK JOURNAL: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan, 1(4), 219–234. https://doi.org/10.54443/sibatik.v1i4.27
Škare, M., Soriano, D. R., & Porada-Rochoń, M. (2021). Impact of COVID-19 on the travel and tourism industry. Technological Forecasting and Social Change, 163, 120469. https://doi.org/10.1016/j.techfore.2020.120469
Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
Sun, S., Wei, Y., Tsui, K.-L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1–10. https://doi.org/10.1016/j.tourman.2018.07.010
Surtiningsih, L. (2017). Prediksi jumlah kunjungan wisatawan mancanegara ke Bali menggunakan support vector regression dengan algoritma genetika. Universitas Brawijaya.
Suryawan, I. W. K., Suhardono, S., Rahman, A., Phan, T. T. T., & Lee, C.-H. (2025). Redesigning Tourism Management Using Hypothetical Scenario Valuation Based on Circular Economy Design Framework. Circular Economy and Sustainability, 5(5), 4027–4053. https://doi.org/10.1007/s43615-025-00581-9
Tovmasyan, G. (2021). Forecasting the number of incoming tourists using Arima model: case study from Armenia. Marketing and Management of Innovations, 5(3), 139–148. https://doi.org/10.21272/mmi.2021.3-12
Vapnik, V. N. (2000). The Nature of Statistical Learning Theory. Springer New York. https://doi.org/10.1007/978-1-4757-3264-1
Zhou, Y., Li, J., Ma, G., & Zhu, Y. (2024). Comparison of parameter optimization methods for a runoff forecast model based on a support vector machine. Physics and Chemistry of the Earth, Parts A/B/C, 135, 103653. https://doi.org/10.1016/j.pce.2024.103653
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