Submit your paper : editorIJETjournal@gmail.com

Paper Title : Prediction of Foreign Currency Exchange (IDR and USD) Using Multiple Linear Regression

ISSN : 2395-1303
Year of Publication : 2020
10.29126/23951303/IJET-V6I3P18

Authors: -Aghistina Kartikadewi, Lina Audina Abdul Rosyid, Anggraeni Eka Putri

         


Abstract
- In developing countries like Indonesia, the stability of currency values is very important to support sustainable economic development and improve people's welfare. the stability of currency values also affects investment activities in every country, including Indonesia. One of them is foreign exchange trading. Foreign exchange trading is one of the financial markets with high liquidity, therefore it is not uncommon for a trader to have a good predictive analysis ability on price changes in the foreign exchange. This is very important so that investors do not experience losses. The instability of the exchange rate can make investors undermine their desire to invest, this will lead to a decline in development in Indonesia because so far the role of foreign investors is very large in economic growth. In this study the types of predictions made are short-term predictions. The data used is one-year data obtained from the Bank Indonesia website. The prediction method used by the author is Multiple Linier Regression. After the implementation and trial results were obtained, the percentage error value using MSE was 165,38%, using MAPE of 24.04% and using a margin error of 25,7%. This exchange rate prediction application can produce predictions that are close to the value of foreign exchange rates issued by Bank Indonesia.

Reference
1. D. Jauhari, A. Hanafi, M. F. A. Y, A. R. Satria, L. H. H, and I. Cholissodin, “Prediksi Nilai Tukar Rupiah Terhadap US Dollar Menggunakan Metode Genetic Programming,” J. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 4, pp. 285–291, 2016. 2. R. J. C. Chen, P. Bloomfield, and F. W. Cubbage, “Comparing Forecasting Models in Tourism,” J. Hosp. Tour. Res., vol. 32, no. 1, pp. 3–21, 2008. 3. R. Amanda, H. Yasin, and A. Prahutama, “Analisis Support Vector Regression (SVR) dalam Memprediksi Kurs Rupiah Terhadap Dolar Amerika Serikat,” J. Gaussian, vol. 3, no. 4, pp. 849–857, 2014. 4. S. D. Anggraini, “Prediksi Nilai Tukar Mata Uang Asing Menggunakan Extreme Learning Machine,” J. Ilm. Mat., vol. 3, no. 6, pp. 110–115, 2017. 5. W. Anggraini and T. Irwinsyah, “Analisis Model Multiple Regression untuk Prediksi Nilai Kurs Rupiah Terhadap Dolar Amerika Berdasarkan Studi Makroekonomi,” J. Has. Penelit. dan Karya Ilm. dalam Bid. Tek. Ind., vol. 3, no. 1, pp. 7–16, 2017. 6. H. Hamdy, Manajemen Keuangan Internasional, 2nd ed. Jakarta: Salemba Empat, 2010. 7. H. Hady, Ekonomi Internasional: Teori dan Kebijakan Keuangan Internasional. Jakarta: Ghalia Indonesia, 2004. 8. I Nyoman Sumerta Yasa, I. K. G. D. Putra, and N. M. A. E. . Wirastuti, “Peramalan Kurs Rupiah Terhadap US Dollar Menggunakan Metode Hibrid,” Teknol. Elektro, vol. 16, no. 3, pp. 33–38, 2017. 9. S. I. Kharomah, I. Rosyida, and Zaenuri, “Implementasi Model Fuzzy-Wavelet dan FIS Metode Mamdani dalam Prediksi Nilai Tukar EUR / IDR,” in PRISMA, Prosiding Seminar Nasional Matematika, 2019, vol. 2, no. ISSN 2613-9189, pp. 313–322. 10. N. Kusumawati, F. Marisa, and I. D. Wijaya, “Prediksi Kurs Rupiah Terhadap Dollar Amerika dengan Menggunakan Metode Regresi Linear,” JIMP - J. Inform. Merdeka Pasuruan, vol. 2, no. 3, pp. 45–56, 2017. 11. D. E. R. Meitasari Winardi Saputri, Wayan Firdaus Mahmudy, “Optimasi Vehicle Routing Problem With Time Window ( Vrptw ) Menggunakan Algoritma Genetika Pada Distribusi Barang,” Repos. J. Mhs. PTIIK Univ. Brawijaya, vol. 5, no. 12, pp. 1–10, 2015. 12. F. A. Minarni, “Prediksi Jumlah Produksi Roti Menggunakan Metode Logika Fuzzy,” J. TEKNOIF, vol. 4, no. 2, pp. 59–65, 2016. 13. N. Nafi’iyah, “Penerapan Regresi Linear dalam Memprediksi Harga Jual Mobil Bekas,” Semin. Nas. Pengemb. Aktual Teknol. Inf. (SENA BAKTI), pp. 1–5, 2015. 14. M. Najwa, B. Warsito, and D. Ispriyanti, “Pemodelan Jaringan Syaraf Tiruan dengan Algoritma One Step Secant Backpropagation dalam Return Kurs Rupiah Terhadap Dolar Amerika Serikat,” J. Gaussian, vol. 6, no. 1, pp. 61–70, 2017. 15. G. S. Y. Raharjo, R. F. Umbara, and D. Triantoro, “Peramalan Nilai Tukar Rupiah Terhadap Dolar Amerika Menggunakan Jaringan Syaraf Tiruan (p,d,q),” J. Telkom, vol. 7, no. 2, pp. 1–7, 2015. 16. F. R. S.N. and Wijanarto, “Prediksi Nilai Tukar Rupiah Berbasis Algoritma Regresi Linier dengan Metode Perbaikan Moving Average dan Seasonal Movement,” Semarang, 2017. 17. D. N. Setiawan, “Prediksi Nilai Tukar Rupiah Terhadap Dolar Amerika dengan Menggunakan Algoritma Genetika - Backpropagation,” Universitas Brawijaya, 2018. 18. C. A. Sugianto and F. Fachruddin, “Prediksi Pergerakan Harga Valas Menggunakan Algoritma Neural Network,” J. Inform. J. Pengemb. IT, vol. 03, no. 01, pp. 20–25, 2018. 19. Triyono, “Analisis Perubahan Kurs Rupiah Terhadap Dollar Amerika,” J. Ekon. Pembang., vol. 9, no. 2, pp. 156–167, 2008.

Keywords
Prediction, Exchange Rate, Foreign Exchange, Linier Regression, Multiple Linier Regression