PREDIKSI KURS MATA UANG RIYAL KE RUPIAH MENGGUNAKAN METODE SUPPORT VECTOR REGRESSION (SVR) - Andika Wahyudi; Wahyudi Setiawan; Yudha Dwi Putra Negara (Universitas Methodist Indonesia, 2024)
Currency exchange rates have an important role in a country's development and economy, especially in international trade and investment. Currency exchange rate fluctuations can have a significant impact on various aspects of the economy. One example is the Rupiah exchange rate against the Saudi Arabian Riyal (SAR), which is important for many Indonesian citizens who undertake the Hajj and Umrah. Currency exchange rate prediction is a complex task because it is influenced by various economic, political, and social factors. Therefore, a method is needed that is able to accommodate the complexity and dynamics of the data. One potential method for predicting currency exchange rates is Support Vector Regression (SVR). SVR is a machine learning method that has demonstrated good performance in various prediction applications due to its ability to handle non-linear data and capture complex patterns in data. This research aims to apply the SVR method to predict the Riyal currency exchange rate against the Rupiah. Prediction accuracy will be measured using Mean Absolute Percentage Error (MAPE). This research is expected to contribute in providing an accurate and efficient tool for predicting the Riyal exchange rate against the Rupiah, providing insight into the application of SVR in currency exchange prediction, and providing practical guidance in implementing the SVR method for the purpose of predicting currency exchange rates. Based on 120 test analyses using the Support Vector Regression (SVR) method using data compositions of 90: 10, 80: 20, and 70: 30, the Mean Absolute Percentage Error (MAPE) value is 0.817317 in testing using a data composition of 90: 10.