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All Journal Jurnal Simantec ELINVO (Electronics, Informatics, and Vocational Education) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi KOMPUTEK Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Science, Engineering and Information Technology Journal Of Information And Technology Unimor (JITU) Journal of Scientech Research and Development Keris: Journal of Community Engagement Insand Comtech : Information Science and Computer Technology Journal Innovative: Journal Of Social Science Research
Yudha Dwi Putra Negara, Yudha
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Journal : METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi

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PREDIKSI KURS MATA UANG RIYAL KE RUPIAH MENGGUNAKAN METODE SUPPORT VECTOR REGRESSION (SVR) Andika Wahyudi; Wahyudi Setiawan; Yudha Dwi Putra Negara
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3307

Abstract

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.
Co-Authors Aeri Rachmad Al-Afghoni, J.M. Havidz Yasaf Anas Al Ayubi, Muhammad Andharini Dwi Cahyani Andika Wahyudi Budi Dwi Satoto Budi Soesilo Budi Soesilo Deykha Aguilika Doni Abdul Fatah Doni Abdul Fatah Faisol Arif Efendi Farhan Reynaldi Valerian Farihatul Mahbubah Fatah, Doni Abdul Fifin Ayu Mufarroha Fifin Ayu Murfarroha Husni Husni Husni i Ifandia, Alfian Mahendra Ika Oktavia Suzanti Jannan, M Fahriz Zain Javier, Najamuddin Khozaimi, Ach. M. Syarief Moh Jufriyanto Muh. Syarif Muhammad Ersya Faraby Muhammad Fikri Muhammad Rafidianto Novi Prastiti, Novi Radiah, Aisyatur Reza Airlangga Rochman, Eka Mala Sari Shofia Hardi Sri Herawati Syakur, Muhammad Ali Uddin, Syah Rafi Umam, Khotibul Vina Angelina Savitri Wahyudi Setiawan