IJASEIT

International Journal on Advanced Science, Engineering and Information Technology, Vol. 13 (2023) No. 2, pages: 694-702, DOI:10.18517/ijaseit.13.2.17944

Forecasting the Currency Rate of The Indonesian Rupiah (IDR) against the US Dollar (USD) Using Time Series Data and Indonesian Fundamental Data

Lady Silk Moonlight, Bambang Bagus Harianto, Yuyun Suprapto, Fiqqih Faizah

Abstract

In forecasting foreign currencies, or known as foreign exchange, fundamental analysis and technical analysis can be used. Fundamental analysis relies on external factors or news happening in the market. In comparison, technical analysis studies the price itself by relying on graphs and mathematical formulas. This study combines fundamental value and technical analysis to predict the Rupiah (IDR) against the Dollar (USD). In this study, the artificial neural network architecture that is compared is Backpropagation and Recurrent Neural Networks (RNN). The RNN architecture used in this research is Elman and Jordan with Backpropagation Through Time (BPTT) learning algorithm. Technical analysis is applied by entering the USD exchange rate against IDR at a certain time. At the same time, fundamental analysis is applied in the form of entering some data on the value of fundamental factors as a training data set. Fundamental data used in this research are inflation rate, interest rate, money supply, and the number of exports and imports in Rupiah. In this study, the prediction system is also compared, the prediction system uses technical data, and the prediction system uses technical and fundamental data. This research results in the prediction system using the Elman RNN algorithm, which is better than the Backpropagation and Jordan RNN algorithms. A prediction system using training data in time series and fundamental data is better than only training data. So, it means in this study that the best prediction system uses the Elman RNN Algorithm with training data in the form of time series data USD sell exchange rate against IDR and fundamental data.