Application of Long Short-Term Memory (LSTM) Algorithm in Predicting Forex Trading Price Movements on the USD/JPY Pair
Keywords:
Long Short-Term Memory (LSTM), Forex, USD/JPY Pair, PredictionAbstract
The foreign exchange (Forex) market offers the potential for high profits but also great risks through currency pair trading. This research proposes the use of the Long Short-Term Memory (LSTM) algorithm, a type of Recurrent Neural Network (RNN), to predict forex price movements for the USD/JPY (American Dollar to Japanese Yen) currency pair based on daily data over a two-year period. The model was designed with a “sequential” architecture consisting of two LSTM layers with 100 units each, followed by a Dropout layer to reduce overfitting and a Dense layer to generate predictions. The total model has 365,905 parameters, with 121,301 parameters trained. During training, model evaluation showed that the combination of batch size 16 and epoch 150 resulted in an RMSE of 0.9840, indicating high accuracy. The application of the model also resulted in an RMSE value of 1.04 and a MAPE of 0.56%, with an average accuracy of 99.44%, indicating a prediction accuracy that successfully follows the actual price trend and is effective in capturing forex price movement patterns in the USD/JPY currency pair, thereby supporting future trading decisions.
References
[1] C. I. Teti Purwanti, “Trader Ini Ungkap Alasan Forex Menguntungkan,” Cnbc Indonesia, 2023. Https://Www.Cnbcindonesia.Com/Mymoney/20231125160247-72-492015/Trader-Ini-Ungkap-Alasan-Forex-Menguntungkan (Accessed Jan. 20, 2024).
[2] Astronacci, “Apa Aja Sih Yang Mempengaruhi Fluktuasi Mata Uang?,” Dcfx, 2023. Https://Www.Dcfx.Co.Id/News/Article/77749 (Accessed Jan. 29, 2024).
[3] G. Article, “Pengertian Trading Forex,” Icdx Group, 2021. Https://Www.Icdx.Co.Id/Id/Gofx-Article/Pengertian-Trading-Forex (Accessed Jan. 29, 2024).
[4] A. Yusuf, “Prediksi Indeks Harga Saham Gabungan (Ihsg) Menggunakan Long Short-Term Memory,” Epsil. J. Mat. Murni Dan Terap., Vol. 15, No. 2, P. 124, 2022, Doi: 10.20527/Epsilon.V15i2.5026.
[5] A. Traderhub, “Yuk Kenali Karakteristik Pair Usdjpy!,” Traderhub, 2021. Https://Traderhub.Id/Yuk-Kenali-Karakteristik-Pair-Usdjpy/ (Accessed Aug. 21, 2024).
[6] A. J. Wijaya, W. Swastika, And O. H. Kelana, “Prediksi Harga Foreign Exchange Mata Uang Eur/Usd Dan Gbp/Usd Menggunakan Long Short-Term Memory,” Sainsbertek J. Ilm. Sains Teknol., Vol. 2, No. 1, Pp. 16–31, 2021, Doi: 10.33479/Sb.V2i1.121.
[7] M. Owen, V. Vincent, R. Br Ambarita, And E. Indra, “Implementasi Metode Long Short Term Memory Untuk Memprediksi Pergerakan Nilai Harga Emas,” J. Tek. Inf. Dan Komput., Vol. 5, No. 1, P. 96, 2022, Doi: 10.37600/Tekinkom.V5i1.507.
[8] M. R. Pahlevi, K. Kusrini, And T. Hidayat, “Prediksi Harga Forex Menggunakan Algoritma Long Short-Term Memory,” Jnananloka, 2022.
[9] B. Samudera, “Memahami Pair Forex Usd/Jpy Dan Karakteristiknya,” Hsb, 2023. Https://Blog.Hsb.Co.Id/Forex/Mengenal-Forex-Pairs-Usd-Jpy-Karakteristiknya/ (Accessed Aug. 21, 2024).
[10] V. P. Ramadhan And F. Y. Pamuji, “Analisis Perbandingan Algoritma Forecasting Dalam Prediksi Harga Saham Lq45 Pt Bank Mandiri Sekuritas (Bmri),” J. Teknol. Dan Manaj. Inform., Vol. 8, No. 1, Pp. 39–45, 2022, Doi: 10.26905/Jtmi.V8i1.6092.
[11] I. Cholissodin And A. A. Soebroto, “Ai , Machine Learning & Deep Learning ( Teori & Implementasi ),” No. December, 2021.
[12] Moch Farryz Rizkilloh And Sri Widiyanesti, “Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (Lstm),” J. Resti (Rekayasa Sist. Dan Teknol. Informasi), Vol. 6, No. 1, Pp. 25–31, 2022, Doi: 10.29207/Resti.V6i1.3630.
[13] Muhammad Haris Diponegoro, Sri Suning Kusumawardani, And Indriana Hidayah, “Tinjauan Pustaka Sistematis: Implementasi Metode Deep Learning Pada Prediksi Kinerja Murid,” J. Nas. Tek. Elektro Dan Teknol. Inf., Vol. 10, No. 2, Pp. 131–138, 2021, Doi: 10.22146/Jnteti.V10i2.1417.
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