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Lady Silk Moonlight
Akademi Teknik dan Keselamatan Penerbangan Surabaya
Aerospace Engineering Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Public Health Transportation Other

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Comparative Analysis of LSTM Neural Network and SVM for USD Exchange Rate Prediction: A Study on Different Training Data Scenarios Rosita, Yesy Diah; Moonlight, Lady Silk
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49975

Abstract

Purpose: This paper aims to investigate and compare the performance of LSTM Neural Network and Support Vector Machines (SVM) in predicting the USD exchange rate using three different training data scenarios: 45%, 55%, and 75%. The study employs a dataset from the Indonesian Central Bureau of Statistics (BPS) for the period of January 1 to June 30, 2021, encompassing attributes USD Selling Rate.Methods: The methods involve implementing LSTM and SVM algorithms within the Python programming language using Google Colaboratory. Three distinct training data scenarios are explored to evaluate the models' robustness. Performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared, are employed for evaluation.Result: Results reveal that LSTM demonstrates superior prediction accuracy compared to SVM across all scenarios, even though it incurs a longer training time. Notably, in the 75% training data scenario, LSTM achieves an MAE of 49.52, RMSE of 63.08, and R-squared of 0.37906, outperforming SVM with MAE of 138.33, RMSE of 161.58, and R-squared of 0.34277.Novelty: This study innovatively compares LSTM Neural Network and Support Vector Machines (SVM) for USD exchange rate prediction across different training scenarios (45%, 55%, and 75%). Unlike previous research focusing on individual models, this study systematically evaluates both methods, highlighting the nuanced balance between prediction accuracy and training time. The findings offer novel insights into LSTM and SVM applicability in currency forecasting, providing valuable guidance for researchers and practitioners in model selection based on specific predictive task requirements.
Co-Authors A.A. Ketut Agung Cahyawan W Ahmad Fudholi Akhwan, Akhwan Arifidin, M. Afif Amalul Budiarto , Anton Dewi Ratna Sari Fatmawati Fatmawati Feryando, Dara Aulia Furyanto, Faoyan Agus Hariyanto, Didi Maulana Anifa Silvia Moch. Rifai Musadek, Ahmad Mustaji Mustaji, Mustaji Oktaria, Dhina Setyo Pangestu, Yohanes Andung Pradipta, Andri Pratiwi, Damar Istri Puspitarini, Erri Wahyu Rifai, Moch. Rochmawati, Laila Soleh, Asep Muhamad Suhanto, Suhanto Sunardi Sunardi Sunaryo Sunaryo Teguh Arifianto Triwijaya, Santi