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To invest or buy and sell on the stock exchange requires understanding in the field of data analysis. The movement of the curve in the stock market is very dynamic, so it requires data modeling to predict stock prices in order to get prices with a high degree of accuracy. One of the steps to achieve this can be using a prediction system based on machine learning. There are several algorithms that can be used to predict stock values, one of which is the Long-Short Term Memory (LSTM) algorithm. This study aims to compare several optimization models, namely the Adam, SGD and RMSprop optimization models to analyze the accuracy of the LSTM algorithm in predicting stock price data and analyzing the number of epochs in forming an optimal model. The results of our research show that the LSTM algorithm has a good level of accurate prediction as shown in the Mean Absolute Percentage Error (MAPE) value and the data model obtained on variations in epochs values. Adam's optimization model shows that the higher the epoch value, the lower the loss value. The lower the loss value, the higher the prediction accuracy of the resulting stock data. Adam's Optimization Model is also the model with the highest accuracy value of 98.45%.