<![CDATA[Economic development in Indonesia has slowed in recent years. This resulted in the movement of the index for several stocks listed on BEIm, especially LQ45 which also experienced increases and decreases. Therefore, it is necessary to analyze stock price movements so that the results of the analysis can be used by investors to make investment decisions. This study will apply several Forecasting algorithms such as Linear Regression and Neural Network to predict the stock price of LQ45 in the case study of Bank Mandiri Sekuritas (BMRI). By using four attributes, namely open, high, and low values as predictors and close as a class, this study focuses on determining the accuracy value, namely Root Mean Squared Error (RMSE) by optimizing parameter values. The test results obtained an RMSE value of 0.034 on the Neural Network method with the addition of a hidden layer and an RMSE value of 0.052 on the Linear Regression method with M5 Prime and Greedy Feature Selection with a min-tolerance value of 0.05.]]>
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