PREDIKSI HARGA CRYPTOCURRENCY XLM MENGGUNAKAN METODE DEEP LEARNING LSTM DAN GRU
PREDICTING XLM CRYPTOCURRENCY PRICES USING LSTM AND GRU DEEP LEARNING MODELS
DOI:
https://doi.org/10.52972/hoaq.vol16no1.p49-58Keywords:
Cryptocurrency, Deep Learning, GRU, LSTM, PrediksiAbstract
Volatilitas pasar yang tinggi serta potensi keuntungan besar dari cryptocurrency menjadikan prediksi harga sebagai topik penelitian yang menarik. Penelitian ini bertujuan untuk memprediksi harga cryptocurrency Stellar (XLM) dengan menerapkan metode Deep Learning, yaitu Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Data yang digunakan mencakup harga harian XLM selama beberapa tahun terakhir, serta indikator teknikal dan aktivitas perdagangan. Model LSTM dan GRU dievaluasi berdasarkan akurasi dalam memprediksi harga XLM menggunakan metrik MAPE, RMSE, dan MSE. Hasil menunjukkan bahwa meskipun keduanya mampu menangkap pola tren jangka pendek, model GRU memberikan hasil yang lebih unggul. GRU mencatat MAPE sebesar 3.6164%, RMSE sebesar 0.0206, dan MSE sebesar 0.0004. Sementara itu, LSTM mencatat MAPE sebesar 4.5638%, RMSE sebesar 0.0244, dan MSE sebesar 0.0005. Temuan ini menunjukkan bahwa GRU lebih efektif dalam memodelkan kompleksitas dan non-linearitas data harga XLM dibandingkan LSTM. Dengan demikian, GRU dapat dipertimbangkan sebagai metode yang lebih unggul dalam prediksi harga cryptocurrency. Hasil penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan model prediksi yang lebih akurat serta membantu pengambilan keputusan investasi yang lebih bijak.
The high market volatility and significant profit potential of cryptocurrencies have made price prediction a compelling area of research. This study aims to predict the price of Stellar (XLM), a widely recognized cryptocurrency, by applying deep learning methods, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The dataset includes daily XLM prices over the past few years, along with technical indicators and trading activity data. The LSTM and GRU models are evaluated based on their accuracy in predicting XLM prices using metrics such as MAPE, RMSE, and MSE. The results show that while both models are capable of capturing short-term trends, the GRU model outperforms LSTM. GRU achieved a MAPE of 3.6164%, RMSE of 0.0206, and MSE of 0.0004, whereas LSTM recorded a MAPE of 4.5638%, RMSE of 0.0244, and MSE of 0.0005. These findings indicate that GRU is more effective in modeling the complexity and non-linearity of XLM price data compared to LSTM. Therefore, GRU can be considered a superior approach for cryptocurrency price prediction. This study is expected to contribute to the development of more accurate forecasting models and to support better investment decision-making.
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