<![CDATA[The Indonesia Stock Exchange (IDX) states that stocks, including LQ45 stocks, which constitute the stock market index for the IDX, have become one of the preferred investment options for the public. Investors need to have accurate analysis and information to gain significant profits as stock prices fluctuate due to company performance, industry factors, changes in interest rates, liquidity, global market conditions, market sentiment, and investor psychology. The Gated Recurrent Unit algorithm is suitable for application on historical stock data sets because they are time series data, can be computed and compared on a numerical scale. This algorithm is a variant of the Long Short-Term Memory algorithm or other types of processing modules for Recurrent Neural Networks. The data set used consists of closing price data or close features, comprising a training data set of 4,406 data and a test data set of 1,889 data that have undergone data preparation using various techniques, including data cleansing, data scrubbing, data splitting, data normalization, and data reshaping. The results showed that the Gated Recurrent Unit algorithm is the right strategy because it obtains a good evaluation of model performance, namely MSE of 0.0009; RMSE of 0.17325 and MAE of 0.0207.]]>
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