<![CDATA[A company needs funding from outside the company so that all aspects of development needed can be fulfilled. Companies that need capital can carry out public offerings and sell securities on a stock exchange company. The movement of stock prices tends to fluctuate, so that it will have an impact on the income that will be received by companies and investors. This problem is currently happening to PT BSI Tbk, so it is necessary to do stock price modeling to predict the value of PT BSI Tbk's stock price in the coming days. Support vector regression is a machine learning method that can deal with fluctuating data by producing good predictive models. SVR aims to find the optimal hyperplane to produce a good predictive model. SVR uses the kernel function to handle non-linear data by mapping data from the input space to a higher feature space, hence it will be easier to form an optimal hyperplane. The kernel function used in this study is the radial basis function. The results of this study are that the best parameters are obtained with C = 100, ϵ = 0.01, and γ = 0.001 and produce a model error accuracy of 0.87%.]]>
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