Forecasting the Currency Rate of The Indonesian Rupiah (IDR) against the US Dollar (USD) Using Time Series Data and Indonesian Fundamental Data
How to cite (IJASEIT) :
F. Leonard and H. Akbar, "Coffee Granularity Classification using Convolutional Neural Network (CNN)," Journal of Applied Science, Engineering, Technology, and Education, vol. IV, no. 1, pp. 133-145, 2022. DOI: 10.35877/454RI.asci842.
Y. You and X. Liu, "Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach," Journal of Banking & Finance, 2020. DOI: 10.1016/j.jbankfin.2020.105849.
Y. Tan, Z. Wang, H. Xiong and Y. Liu, "Fundamental momentum and enhanced fundamental momentum: Evidence from the Chinese stock market," International Review of Economics & Finance, pp. 680-693, 2022. DOI: 10.1016/j.iref.2022.02.012.
M. D. Bianco, M. Camacho and G. P. Quiros, "Short-run forecasting of the euro-dollar exchange rate with economic fundamentals," Journal of International Money and Finance, pp. 377-396, 2012. DOI: 10.1016/j.jimonfin.2011.11.018.
M. M. Kumbure, C. Lohrmann, P. Luukka and J. Porras, "Machine learning techniques and data for stock market forecasting: A literature review," Expert Systems With Applications, 2022. DOI: 10.1016/j.eswa.2022.116659.
O. F. Beyca, B. C. Ervural, E. Tatoglu, P. G. Ozuyar and S. Zaim, "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, 2019. DOI: 10.1016/j.eneco.2019.03.006.
L. Nevasalmi, "Forecasting multinomial stock returns using machine learning methods," The Journal of Finance and Data Science, pp. 86-106, 2020. DOI: 10.1016/j.jfds.2020.09.001.
A. Dubois, F. Teytaud and S. Verel, "Short term soil moisture forecasts for potato crop farming: A machine learning approach," Computers and Electronics in Agriculture, 2021. DOI: 10.1016/j.compag.2020.105902.
G. K. Vishwakarma, C. Paul and A. M. Elsawah, "An algorithm for outlier detection in a time series model using backpropagation neural network," Journal of King Saud University, pp. 3328-3336, 2020. DOI: 10.1016/j.jksus.2020.09.018.
M. Elbayoumi, N. A. Ramli, N. Faizah and F. M. Yusof, "Development and comparison of regression models and feedforward backpropagation neural network models to predict seasonal indoor PM2.5-10 and PM2.5 concentrations in naturally ventilated schools," Atmospheric Pollution Research, pp. 1013-1023, 2015. DOI: 10.1016/j.apr.2015.09.001.
R. Olawoyin, "Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil," Chemosphere, pp. 145-150, 2016. DOI: 10.1016/j.chemosphere.2016.07.003.
C. Liu, "Risk Prediction of Digital Transformation of Manufacturing Supply Chain Based on Principal Component Analysis and Backpropagation Artificial Neural Network," Alexandria Engineering Journal, vol. 61, no. 1, pp. 775-784, 2022. DOI: 10.1016/j.aej.2021.06.010.
Y.-L. Wang, H. Jahanshahi, S. Bekiros, F. Bezzina, Y.-M. Chu and A. A. Alyh, "Deep recurrent neural networks with finite-time terminal sliding mode control for a chaotic fractional-order financial system with market confidence," Chaos, Solitons and Fractals, vol. 146, 2021.
Y. Song and Y. Wang, "A big-data-based recurrent neural network method for forest energy estimation," Sustainable Energy Technologies and Assessments, vol. 55, 2023. DOI: 10.1016/j.seta.2022.102910.
M. Villegas, A. Gonzalez-Agirre, A. Gutií©rrez-Fandiño, J. Armengol-Estapí©, C. P. Carrino, D. Pí©rez-Ferní¡ndez, F. Soares, P. Serrano, M. Pedrera, N. García and A. Valencia, "Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach," Computer Methods and Programs in Biomedicine, vol. 3, 2023. DOI: 10.1016/j.cmpbup.2022.100089.
P. R. Vlachas, J. Pathak, B. Hunt, T. Sapsis, M. Girvan, E. Ott and P. Koumoutsakosa, "Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics," Neural Networks, vol. 126, pp. 191-217, 2020. DOI: 10.1016/j.neunet.2020.02.016.
M. S. Alhajeri , Z. Wu, D. Rincon, F. Albalawi and P. D. Christoï¬des, "Estimation-Based Predictive Control of Non-linear Processes Using Recurrent Neural Networks," IFAC PapersOnLine, vol. 54, no. 3, p. 91-96, 2021. DOI: 10.1016/j.ifacol.2021.08.224.
X. Zhang, C. Zhong, J. Zhang, T. Wang and W. W. Y. Ng, "Robust Recurrent Neural Networks for Time Series Forecasting," Neurocomputing, 2023. DOI: 10.1016/j.neucom.2023.01.037.
S. Li and Y. Yang, "A recurrent neural network framework with an adaptive training strategy for long-time predictive modeling of non-linear dynamical systems," Journal of Sound and Vibration, vol. 506, 2021. DOI: 10.1016/j.jsv.2021.116167.
H. Hewamalage, C. Bergmeir and K. Bandara, "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, vol. 37, no. 1, pp. 388-427, 2021. DOI: 10.48550/arXiv.1909.00590.
Y. Wang, L. Wang, F. Yang, W. Di and Q. Chang, "Advantages of direct input-to-output connections in neural networks: The Elman network for stock index forecasting," Information Sciences, pp. 1066-1079, 2021. DOI: 10.1016/j.ins.2020.09.031.
Q. Meng, X. Yuan, X. Ren, H. Li, L. Jiang and L. Yang, "Thermal Energy Storage Air-conditioning Demand Response Control Using Elman Neural Network Prediction Model," Sustainable Cities and Society, 2022. DOI: 10.1016/j.scs.2021.103480.
A. Zaras, N. Passalis and A. Tefas, Deep Learning for Robot Perception and Cognition, Academic Press, 2022.
L. S. Moonlight and A. S. Prabowo, "Forecasting System for Passenger, Airplane, Luggage and Cargo, Using Artificial Intelligence Method-Backpropagation Neural Network at Juanda International Airport," Warta Ardhia Jurnal Perhubungan Udara, vol. 45, no. 2, pp. 99-110, 2019. DOI: 10.25104/wa.v45i2.358.99-110.
L. S. Moonlight, F. Faizah, Y. Suprapto and N. Pambudiyatno, "Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition," Journal of Information Systems Engineering and Business Intelligence, vol. 7, no. 2, pp. 149-161, 2021. DOI: 10.20473/jisebi.7.2.149-161.
A. Y. Alanis, N. Arana-Daniel and C. López-Franco, Artificial Neural Networks for Engineering Applications, Guadalajara, Mexico: Academic Press, 2019.
A. Subasi, Practical Machine Learning for Data Analysis Using Python, Jeddah, Saudi Arabia: Academic Press, 2020.
M. Gori, Machine Learning - A Constraint-Based Approach, Morgan Kaufmann, 2018.
K. Bandara, C. Bergmeir and S. Smyl, "Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach," Expert Systems with Applications, vol. 140, 2020. DOI: 10.48550/arXiv.1710.03222.
S. Achanta and S. V. Gangashetty, "Deep Elman recurrent neural networks for statistical parametric speech synthesis," Speech Communication, vol. 93, pp. 31-42, 2017. DOI: 10.1016/j.specom.2017.08.003.
A. Glassner, Deep Learning : A Visual Approach, San Francisco: Willian Pollock, 2021.
J. D. Rios, A. Y. Alanis, N. Arana-Daniel, C. Lopez-Franco and E. N. Sanchez, Neural Networks Modeling and Control - Applications for Unknown Non-linear Delayed Systems in Discrete Time, Jalisco, Mexico: Academic Press, 2020.
A. Mechelli and S. Vieira, Machine Learning - Methods and Applications to Brain Disorders, London, United Kingdom: Academic Press, 2019.
Z. Al-Ghazawi and R. Alawneh, "Use of artificial neural network for predicting effluent quality parameters and enabling wastewater reuse for climate change resilience - A case from Jordan," Journal of Water Process Engineering, vol. 44, 2021. DOI: 10.1016/j.jwpe.2021.102423.
P. Kumar, Y. Kumar and M. A. Tawhid, Machine Learning, Big Data, and IoT for Medical Informatics, Academic Press, 2021.
R. L. Kissell, Algorithmic Trading Methods - Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques, NY, United States: Academic Press, 2020.
BPS, "Indeks Harga Konsumen dan Inflasi Bulanan Indonesia," 20 January 2022. [Online]. Available: https://www.bps.go.id/statictable/2009/06/15/907/indeks-harga-konsumen-dan-inflasi-bulanan-indonesia-2006-2022.html.
BPS, "Nilai Ekspor Migas-NonMigas," 15 January 2022. [Online]. Available: https://www.bps.go.id/indicator/8/1753/1/nilai-ekspor-migas-nonmigas.html.
BPS, "Nilai Impor Migas-NonMigas," 10 January 2022. [Online]. Available: https://www.bps.go.id/indicator/8/1754/2/nilai-impor-migas-nonmigas.html.
BI, "Bank Indonesia - Bank Sentral Republik Indonesia," 5 January 2022. [Online]. Available: https://www.bi.go.id/id/statistik/informasi-kurs/transaksi-bi/default.aspx.
BPS, "Uang Beredar," 8 January 2022. [Online]. Available: https://www.bps.go.id/indicator/13/123/1/uang-beredar.html.

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).