Integrating Whale Transaction Flow Scoring with LSTM for Bitcoin Trend Forecasting - Hakiki, Muhammad Ridhwan; Kusnawi, Kusnawi (Jurnal Teknik Informatika (Jutif), 2026)
Bitcoin price prediction faces significant challenges due to high volatility and the influence of large holders, known as whales, whose transactions exceeding 500 BTC can affect market behavior. This study develops an LSTM model combining whale transaction sentiment scores with historical Bitcoin OHLC prices to forecast 7-day ahead price movements. The dataset comprises 2,069 whale transactions and 8,761 hourly price observations from April 20, 2024 to April 20, 2025. The scoring mechanism assigns +1 to exchange outflows, -1 to inflows, and 0 to neutral transfers, multiplied by logarithmically normalized transaction amounts. The LSTM architecture consists of two recurrent layers with 128 and 64 memory units, processing 720-hour input sequences to generate 168-hour OHLC forecasts. Training evaluation yielded R² of 0.9386, RMSE of 0.0686, and MAE of 0.0498. Test evaluation produced Mean Absolute Errors ranging from 871.72 USD to 3,482.27 USD across OHLC components. The model correctly predicted upward directional trends but systematically underestimated prices by 2,000-3,000 USD initially and failed to anticipate a 6,422 USD intraday surge on April 22, 2025. Results demonstrate that whale sentiment features enhance directional trend identification but do not enable precise multi-day price point prediction due to sudden market regime changes. These findings contribute empirical evidence that directional sentiment scoring of large-holder transactions provides complementary predictive value beyond conventional price-volume indicators, establishing a methodological foundation for integrating blockchain-native behavioral signals into cryptocurrency forecasting frameworks.