Implementasi Model Long Short-Term Memory Berbasis RSI dan MACD untuk Prediksi Saham BBCA
Abstract
This study enhances BBCA stock price forecasting accuracy by overcoming standalone historical data limitations in tracking market momentum. The methodology integrates a Long Short-Term Memory (LSTM) model with Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) technical indicators. Adopting the CRISP-DM framework, BBCA data from 2018 to 2026 were gathered via yfinance, followed by feature engineering and Min-Max Scaler normalization. The structured LSTM model, utilizing three input features and 60-day time steps, was evaluated using RMSE and MAPE metrics. The network achieved an RMSE of IDR 173.99 and a MAPE of 1.56%, satisfying the sub-10% threshold for highly accurate predictions. These outcomes prove that merging RSI and MACD significantly improves LSTM's trend-capturing capability. This hybrid framework is highly viable as an investment decision support system.
Keywords: Stock Prediction; BBCA; LSTM; RSI; MACD
Abstrak
Penelitian ini meningkatkan akurasi prediksi harga saham BBCA dengan mengatasi keterbatasan data historis tunggal dalam membaca momentum pasar. Metodologi yang digunakan mengintegrasikan model Long Short-Term Memory (LSTM) dengan indikator teknikal Relative Strength Index (RSI) dan Moving Average Convergence Divergence (MACD). Mengadopsi kerangka CRISP-DM, data BBCA periode 2018 hingga 2026 diambil melalui yfinance, dilanjutkan rekayasa fitur dan normalisasi Min-Max Scaler. Model LSTM yang dibangun menggunakan tiga fitur input dan time steps 60 hari dievaluasi dengan metrik RMSE dan MAPE. Jaringan ini menghasilkan RMSE sebesar Rp173,99 dan MAPE 1,56%, memenuhi ambang batas di bawah 10% untuk kategori prediksi sangat akurat. Hasil ini membuktikan penggabungan RSI dan MACD secara signifikan meningkatkan kemampuan LSTM dalam menangkap tren. Sistem hibrida ini sangat layak diimplementasikan sebagai pendukung keputusan investasi.
Keywords
References
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