CHINESE YUAN EXCHANGE RATE AGAINST THE INDONESIAN RUPIAH PREDICTION USING SUPPORT VECTOR REGRESSION

  • Steven Soewignjo Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Ni Wayan Widya Septia Sari Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Andini Putri Mediani Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • M. Aqil Zaidan Kamil Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Dita Amelia Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia https://orcid.org/0000-0002-2387-9981
  • Nur Chamidah Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia https://orcid.org/0000-0003-1592-4671
Keywords: Support Vector Regression, Yuan, Prediction, Rupiah, Currency

Abstract

This study aims to forecast the exchange rate between the Chinese Yuan (CNY) and the Indonesian Rupiah (IDR) using Support Vector Regression (SVR), a machine-learning technique that can handle nonlinear and complex data. The authors utilize the monthly selling exchange rate of CNY against IDR from January 2012 to October 2023 sourced from the “investing” platform. The optimal SVR model is obtained by splitting the data into 113 training samples and 28 testing samples and using the Radial Basis Function (RBF) kernel. The model achieves high accuracy, with a Mean Absolute Percentage Error (MAPE) of 1.738%, a Root Mean Squared Error (RMSE) of 50.661 for the training data and a MAPE of 2.516%, and an RMSE of 64.735 for the testing data. The results of this paper can provide valuable insights for policymakers, investors, and traders who are interested in the CNY/IDR exchange rate dynamics and the economic implications of the Belt and Road Initiative (BRI). The study aligns with the Sustainable Development Goals (SDGs), specifically SDG 8, aiming to promote sustained, inclusive, and sustainable economic growth.

Downloads

Download data is not yet available.

References

N. Dewi, “Pengaruh Ekspor, Impor, Inflasi, dan Pertumbuhan Ekonomi terhadap Nilai Tukar di Indonesia,” Jurnal Ekonomi Daerah (JEDA), vol. 8, no. 2, 2020.

S. W. R, T. S. Putro, and A. Mayes, “Pengaruh Produk Domestik Bruto, Inflasi Dan Capital Account Terhadap Nilai Tukar Rupiah Atas Dollar Amerika Serikat Periode Tahun 2001-2014,” Jurnal Online Mahasiswa Fakultas Ekonomi Universitas Riau, vol. 2, no. 2, pp. 1–10, 2015.

S. Djankov, C. S. Hendrix, R. Z. Lawrence, S. Miner, E. M. Truman, and F. Toohey, China’s Belt and Road Initiative: Motives, Scope, and Challenges. Peterson Institute for International Economics (PIIE), 2016. [Online]. Available: http://www.piie.com/institute/supporters.pdf.

G. P. Octorifadli, A. Puspitasari, and A. A. R. Azzqy, “Kepentingan Tiongkok terhadap Indonesia melalui Belt and Road Initiative dalam Pembangunan Kereta Cepat Jakarta - Bandung Periode 2015-2020,” Balcony: Budi Luhur Journal of Contemporary Diplomacy, vol. 5, no. 2, pp. 175–186, 2021.

Green Finance and Development Center, “Countries of the Belt and Road Initiative (BRI) – Green Finance & Development Center,” Green Finance and Development Center. Accessed: Dec. 31, 2023. [Online]. Available: https://greenfdc.org/countries-of-the-belt-and-road-initiative-bri/?cookie-state-change=1704033449441

H. Kreinin and E. Aigner, “From ‘Decent work and economic growth’ to ‘Sustainable work and economic degrowth’: a new framework for SDG 8,” Empirica, vol. 49, no. 2, pp. 281–311, May 2022, doi: 10.1007/s10663-021-09526-5.

R. Amanda, H. Yasin, and A. Prahutama, “Analisis Support Vector Regression (SVR) dalam Memprediksi Kurs Rupiah terhadap Dollar Amerika Serikat,” Jurnal Gaussian, vol. 3, no. 4, pp. 849–858, Oct. 2014, doi: https://doi.org/10.14710/j.gauss.3.4.849-858.

M. das C. Moura, E. Zio, I. D. Lins, and E. Droguett, “Failure and Reliability Prediction by Support Vector Machines Regression of Time Series Data,” Reliab Eng Syst Saf, vol. 96, no. 11, pp. 1527–1534, Nov. 2011, doi: 10.1016/j.ress.2011.06.006.

Investing, “CNY IDR | Chinese Yuan Indonesian Rupiah - Investing.com.” Accessed: Dec. 31, 2023. [Online]. Available: https://www.investing.com/currencies/cny-idr

R. G. Brereton and G. R. Lloyd, “Support Vector Machines for Classification and Regression,” Analyst, vol. 135, no. 2, pp. 230–267, 2010, doi: 10.1039/B918972F.

Z. Chen and W. Liu, “An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm,” IEEE Access, vol. 8, pp. 156851–156862, 2020, doi: 10.1109/ACCESS.2020.3018866.

J. Wu and Y.-G. Wang, “A working likelihood approach to support vector regression with a data-driven insensitivity parameter,” Mar. 2020.

R. P. Furi, M. S. Jondri, and D. Saepudin, “Prediksi Financial Time Series Menggunakan Independent Component Analysis dan Support Vector Regression Studi Kasus : IHSG dan JII,” Universitas Telkom, Bandung, 2015.

E. Ceperic, V. Ceperic, and A. Baric, “A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4356–4364, Nov. 2013, doi: 10.1109/TPWRS.2013.2269803.

A. Hermawan, I. W. Mangku, N. K. K. Ardana, and H. Sumarno, “ANALISIS SUPPORT VECTOR REGRESSION DENGAN ALGORITMA GRID SEARCH UNTUK MEMPREDIKSI HARGA SAHAM,” Journal of Mathematics and Its Applications, vol. 18, no. 1, pp. 41–60, Jul. 2022, doi: 10.29244/milang.18.1.41-60.

F. Zhou, “Cross-validation research based on RBF-SVR model for stock index prediction,” Data Science in Finance and Economics, vol. 1, no. 1, pp. 1–20, 2021, doi: 10.3934/DSFE.2021001.

D. Wang, X. Ban, L. Ji, X. Guan, K. Liu, and X. Qian, “An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount,” Comput Intell Neurosci, vol. 2020, pp. 1–15, May 2020, doi: 10.1155/2020/7986982.

A. Costa, E. Di Buccio, M. Melucci, and G. Nannicini, “Efficient Parameter Estimation for Information Retrieval Using Black-Box Optimization,” IEEE Trans Knowl Data Eng, vol. 30, no. 7, pp. 1240–1253, Jul. 2018, doi: 10.1109/TKDE.2017.2761749.

F. Nuryana, “Pemodelan Data Deret Waktu Dengan Self Exciting Treshold Autoregressive (SETAR) dan Perubahan Struktur - Modelling Time Series Data with Self Exciting Treshold Autoregressive (SETAR) and Structural Change,” Institut Teknologi Sepuluh Nopember, Surabaya, 2009. Accessed: Oct. 04, 2023. [Online]. Available: https://repository.its.ac.id/60141/

D. Ruswanti, “PENGUKURAN PERFORMA SUPPORT VECTOR MACHINE DAN NEURAL NETWOK DALAM MERAMALKAN TINGKAT CURAH HUJAN,” Gaung Informatika, vol. 13, no. 1, 2020, doi: 10.47942/gi.v13i1.455.

S. Kim and H. Kim, “A new metric of absolute percentage error for intermittent demand forecasts,” Int J Forecast, vol. 32, no. 3, pp. 669–679, Jul. 2016, doi: 10.1016/j.ijforecast.2015.12.003.

Published
2024-08-02
How to Cite
[1]
S. Soewignjo, N. W. Septia Sari, A. Mediani, M. A. Kamil, D. Amelia, and N. Chamidah, “CHINESE YUAN EXCHANGE RATE AGAINST THE INDONESIAN RUPIAH PREDICTION USING SUPPORT VECTOR REGRESSION”, BAREKENG: J. Math. & App., vol. 18, no. 3, pp. 1683-1694, Aug. 2024.