Perbandingan Fuzzy Mamdani dan Sugeno dalam Optimasi Trading Bitcoin Berbasis Indikator Teknikal - Dwi Rahmadewi, Cynthia; Parlika, Rizky; Maulana, Hendra (JURNAL FASILKOM, 2026)
This study compares Mamdani and Sugeno fuzzy inference systems for Bitcoin trading using historical BTC/USDT data. In highly volatile and non-linear cryptocurrency markets, especially during bear markets, conventional methods struggle to interpret ambiguous signals, making fuzzy logic suitable for adaptive decision-making. The dataset was collected from the Binance API for the period 20 November 2021 to 31 December 2022 and consists of 9,746 candlestick records. This period corresponds to a bear market phase, characterized by a significant downward trend in Bitcoin prices, which provides a challenging environment for evaluating trading strategies. Four technical indicators, Bollinger Bands, RSI, ADX, and PSAR, were used as input variables. The data were split into 70% training and 30% testing using a time-based approach. Performance evaluation was conducted through long-only backtesting using Total Profit, Win Rate, Maximum Drawdown, Sharpe Ratio, and Sortino Ratio. The results show that Mamdani achieved better profitability than Sugeno, with total profit of -34.17% on training data and -2.45% on testing data, while Sugeno produced -53.91% and -3.04%, respectively. Although both methods resulted in negative returns due to the bearish market conditions, their performance was better than the buy-and-hold strategy, which recorded losses of -65.78% on training data and -17.49% on testing data. This indicates that both fuzzy approaches were effective in reducing losses and improving risk management under extreme market conditions. However, Sugeno showed better risk control on testing data with a lower maximum drawdown of 18.72% compared to 25.01% for Mamdani. Overall, Mamdani is more suitable for return-oriented strategies, while Sugeno is more appropriate for risk management under bearish conditions.