Proceedings of the 2nd International Conference on Law, Social Science, Economics, and Education, ICLSSEE 2022, 16 April 2022, Semarang, Indonesia

Research Article

Analysis of Foreign Exchange Using Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS)

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  • @INPROCEEDINGS{10.4108/eai.16-4-2022.2319723,
        author={Yulius Eka Agung Seputra and Ahmad  Rodoni and Meirinaldi  Meirinaldi},
        title={Analysis of Foreign Exchange Using Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS)},
        proceedings={Proceedings of the 2nd International Conference on Law, Social Science, Economics, and Education, ICLSSEE 2022, 16 April 2022, Semarang, Indonesia},
        publisher={EAI},
        proceedings_a={ICLSSEE},
        year={2022},
        month={8},
        keywords={technical analysis; fundamental analysis; investment decision making; trading transactions; forex; forex trading},
        doi={10.4108/eai.16-4-2022.2319723}
    }
    
  • Yulius Eka Agung Seputra
    Ahmad Rodoni
    Meirinaldi Meirinaldi
    Year: 2022
    Analysis of Foreign Exchange Using Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS)
    ICLSSEE
    EAI
    DOI: 10.4108/eai.16-4-2022.2319723
Yulius Eka Agung Seputra1,*, Ahmad Rodoni2, Meirinaldi Meirinaldi2
  • 1: Student of Doctoral Program in Economics, Borobudur University
  • 2: Borobudur University
*Contact email: yulius.eka@gmail.com

Abstract

Foreign Exchange (FOREX) is the exchanging of one money against another. FOREX rates are impacted by many related monetary, political and mental variables and subsequently anticipating it is a difficult errand. A few techniques to foresee the FOREX rate incorporate measurable examination, time series investigation, fluffy frameworks, brain organizations, and mixture frameworks. These techniques experience the ill effects of the issue of precisely foreseeing the trade. An Artificial Neural Network (ANN) and a cross breed Neuro-Fuzzy framework (ANFIS) are proposed to foresee the future pace of the FOREX market because can combine fundamental and technical FOREX Data. The independent variables studied in this study were the exchange rates of China, Japan, Europe, Gold and Crude Oil to analyze the Rupiah exchange rate dependent variable. For the analysis, USDIDR swapping scale from the forex market is utilized. Mean Square Error (MSE) and Mean Absolute Error (MAE) are utilized as execution pointers. ANFIS model accomplished a MSE of 0.02 and a MAE of 0.00792 during the preparing and testing stage.