APPLICATION OF THE FUZZY TIME SERIES CHEN MODEL IN FORECASTING THE RUPIAH EXCHANGE RATE AGAINST THE US DOLLAR (USD)
APPLICATION OF THE FUZZY TIME SERIES CHEN MODEL IN FORECASTING THE RUPIAH EXCHANGE RATE AGAINST THE US DOLLAR (USD)
DOI:
https://doi.org/10.31258/jsmds.v1i2.11Keywords:
american dollars (USD) , forecasting , FTS Chen , rupiah exchange rateAbstract
The importance of the rupiah exchange rate for many individuals in Indonesia in everyday life can be explained by the fact that the country's economy is very dependent on the international economy. Exchange rate used to do future payments using a certain currency and so on the link between two currencies of different countries. In context In international trade, the US Dollar (USD) currency has a very important role very significantly for developing countries because it is used as an eye transaction money. Therefore, the movement of the rupiah exchange rate is an important factor for a country. So forecasting techniques are needed to anticipate exchange rate changes. In this study, the method used is Chen's Fuzzy Time Series (FTS) model to predict the rupiah exchange rate against the United States Dollar (USD) in the future. The results of this study show that forecasting the rupiah exchange rate against the US Dollar (USD) from May 2023 to July 2023 is stable at a value of 1412.40 Rupiah, with a rate average absolute error (MAPE) of 1.6717%.
References
Amelia, RR, & Fitri, F. (2022). Forecasting the Rupiah Exchange Rate Against the US Dollar Using Artificial Neural Networks.7 (3), 1–10.
Ekananta, Y., Muflikhah, L., & Dewi, C. (2018). Application of the Average-Based Fuzzy Time Series Method to Predict Indonesia's Electrical Energy Consumption . 2 (3), 1283–1288.
Elvierayani, RR (2017). Forecasting the Rupiah Exchange Rate against the Dollar in 2017 using the Arima Box-Jenkins Method. Proceedings of SI MaNIs (National Seminar on the Integration of Mathematics and Islamic Values). 1 (1), 253–261. http://conferences.uin malang.ac.id/index.php/SIMANIS/article/view/80
Hafiyya, N., Virgantari, F., & Widyastiti, M. (2022). Implementation Of The Fuzzy Time Series Method In . 2 (2), 94–103.
Hansun, S. (2013). Forecasting IHSG Data Using Fuzzy Time Series. IJCCS (Indonesian Journal Computing and Cybernetics Systems). 7 (1), 79–88. https://doi.org/10.22146/ijccs.2155
Hidayati, I., & Asikin, M. (2014). Unnes Journal of Mathematics Education. Ujme. 3 (2), 4–7. http://journal.unnes.ac.id/sju/index.php/ujme
Iswardani, PR, Sudarma, M., & Jasa, L. (2021). Forecasting the Rupiah Exchange Rate against Asian Currencies Using the Quantum Neural Network Method. Electrical Technology Scientific Magazine. 20 (1), 153. https://doi.org/10.24843/mite.2021.v20i01.p18
Tri, A., Dani, R., Adrianingsih, NY, Ainurrochmah, A., Statistics, D., Nonparametric, R., & Truncated, S. (2020). Jambura Journal of Probability And Statistics. 1(2)
