Pengembangan Analisis Teknikal Untuk Trading Bursa Saham dengan Long Short Term Memory - Faris Abdi El Hakim; Arna Fariza; Setiawardhana Setiawardhana (Universitas Budi Darma, 2023)
Stock price movements are difficult to predict and can change over time. Technical analysis is necessary to determine the right timing and company for stock investments. However, novice traders may face difficulties in analyzing stocks. Therefore, the author conducted research on the development of technical analysis for stock trading using Long Short-Term Memory (LSTM). The study utilized data from PT Bank Central Asia Tbk (BBCA.JK) stock prices, covering the period from July 1, 2004, to April 28, 2023. The LSTM method combined with technical analysis resulted in RMSE values of 136.759 for Open price, 126.52 for Close price, 317.968 for High price, 178.001 for Low price, and 189.669 for Adj. Close price, which outperformed the LSTM method without technical analysis. Furthermore, the LSTM method achieved better accuracy compared to Support Vector Regression (SVR) and K-Nearest Neighbors (KNN) using three different datasets. The RMSE values were 65.21 for LSTM, 313.56 for SVR, and 72.44 for KNN. The R2 values were 0.9919 for LSTM, 0.81 for SVR, and 0.990 for KNN. The results of the model were implemented in a web-based system using the Laravel framework and MySQL database.