PREDIKSI HARGA PENUTUPAN HARIAN ETHEREUM TERHADAP RUPIAH MENGGUNAKAN RANDOM FOREST DAN INDIKATOR TEKNIKAL

Dimas Danni Arganata; Mohammad Zainudin

doi:10.47111/jti.v20i1.23551

Authors

Dimas Danni Arganata Institut Asia Malang
Mohammad Zainudin Institut Asia Malang

DOI:

https://doi.org/10.47111/jti.v20i1.23551

Keywords:

ethereum, Cryptocurrency Forecasting, Random Forest Regression, Technical Indicator, Machine Learning

Abstract

This research aims to develop a predictive model for estimating the daily closing price of Ethereum (ETH) against the Indonesian Rupiah (IDR) using the Random Forest Regression algorithm. Ethereum is one of the most widely traded cryptocurrencies and is known for its high volatility, which makes accurate price prediction essential for supporting data-driven investment decisions. Historical price data were collected from the CoinGecko API for a period of 365 days, followed by preprocessing, feature engineering, and the computation of several technical indicators including Exponential Moving Average (EMA-14), Relative Strength Index (RSI-14), Daily Return, Bollinger Bands Upper, Average True Range (ATR-14), and Close Lag-1.The research starting from data selection and preprocessing to modeling, evaluation and visualization. Random Forest Regression was chosen due to its robustness in handling nonlinear relationships and noisy time-series data. The dataset was split using a 90:10 time-based hold-out method, and model performance was evaluated using four regression metrics: MAE, RMSE, MAPE, and R-squared. The best configuration of the model achieved a MAPE of 2.88%, indicating a high level of predictive accuracy. Feature importance analysis shows that Daily Return and ATR-14 contributed most significantly to the prediction. The findings demonstrate that Random Forest Regression can effectively capture the nonlinear patterns in cryptocurrency price movements, providing an accurate and reliable model for short-term forecasting. This model may serve as a valuable reference for investors, financial analysts, and developers of automated trading systems.

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DOI: 10.47111/jti.v20i1.23551 DOI URL: https://doi.org/10.47111/jti.v20i1.23551

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