Abstract
Investment is one of the means provided by the Indonesia Stock Exchange (BEI) to offer an alternative capital
market (stocks) to the public, especially investors. PT Bank MNC has developed as a leading bank in Indonesia that considers economic, social, and environmental interests. Over time, stock movements are relatively unstable and uncertain, yet still predictable. Accurate prediction models of stock price movements can assist investors in making stock trading decisions because stock price movements are usually nonlinear, making it challenging for investors to predict. This study uses the Support Vector Machine Gaussian Rbf algorithm and Confusion Matrix to predict the stock price of MNC Bank based on the attributes present in the data. An 80% training data set is used for training, while 20% of the data is used as a test set. The accuracy results obtained are very high, reaching 97%. Therefore, it can be concluded that the Confusion Matrix and Support Vector Machine methods are good prediction methods in the field of buying and selling stock prices, especially in predicting the stock prices of MNC Bank. The high level of accuracy indicates the good performance of these methods.

Keywords: Prediction, stocks, MNC BANK, SVM, Investment.