The stock market price movement has become an interesting topic to research due to the large profit potential. Market traders use technical analysis to forecast stock prices by analyzing stock transaction data. Because technical analysis predictions are subjective, an automated system is needed to produce more objective and faster results. In the previous, next prices were predicted by combining price and transaction volume features with technical analysis indicators such as Stochastic, Moving Average, Moving Average Convergence Divergence, and Relative Strength Index values. In most cases, the addition of the indicator's value reduces the accuracy of the forecast. This is because the value of technical analysis indicators is not useful enough when they are used directly without transformation. The transformation of technical analysis indicators to a crossing value between the fast and slow signal on these indicators, known as the golden cross and death cross, has a greater impact on stock price movement. Therefore, it is recommended to use the price and the golden cross and death cross on technical analysis indicators as inputs to improve stock price predictions. Long Short Term Memory is used in the proposed method, which is a reliable method for predicting sequence data. The stock price forecasts are based on 1200 days of trading data from the Indonesia Stock Exchange for 10 LQ45 group shares. The test was conducted by calculating the Root Mean Squared Error and the Mean Absolute Percentage Error value of the predicted close price next day.