Prediksi Harga Cryptocurrency Binance Berdasarkan Informasi Blokchain dengan Menggunakan Algoritma Random Forest - Jumjumi Asbullah; Samsudin Samsudin (Universitas Budi Darma, 2024)
This study proposes the use of the Random Forest algorithm to forecast the cryptocurrency Binance's daily prices. With a dataset covering 1992 observations from January 1, 2018, to June 15, 2023, the research focuses on PT. Tennet Depository Indonesia. Through Python implementation, the experimental results indicate that Random Forest is effective in providing accurate price predictions, with an average Mean Absolute Percentage Error (MAPE) of approximately 1.38% and an average Root Mean Squared Error (RMSE) of about 4.38. The uniqueness of the system lies in the algorithm's capability to handle market complexities and volatility, offering adaptive solutions to the unpredictable dynamics of the market. Nevertheless, limitations in historical data and market volatility persist as inhibiting factors, emphasizing the need for a holistic approach. The average MAPE and RMSE results provide an indication of the overall reliability of the model in facing cryptocurrency market volatility. These conclusions can contribute to the development of more robust and adaptive models to respond to the evolving market conditions.