Optimization of a Long Short-Term Memory Algorithm for Predicting Stock Prices: A Case Study of the State-Owned Enterprises Banking Sector

The ease of internet access allows people to utilize investment products anywhere and anytime. Stocks are one such investment product, allowing individuals to own a portion of a company. One of the factors influencing stock price fluctuations is technical analysis, which relies on previous stock price data. The Long Short-Term Memory (LSTM) algorithm has the advantage of storing data or information over the long term by using gates within LSTM, including the forget gate, input gate, and output gate. Three datasets of stock prices from state-owned enterprise banks, namely Bank BRI, Bank BNI, and Bank Mandiri, were used, totaling 4479 rows of data across the three banks. From these datasets, the optimal values for the LSTM algorithm were obtained, with Mean Absolute Percentage Error (MAPE) values ranging from 0.01 to 0.025 and R2 values ranging from 0.8 to 0.96.