Analysis of Stock Price Trend Prediction Using the Support Vector Machine (SVM) Method with the Moving Average Indicator at PT Bukit Asam Tbk
Keywords:
Predicting Tren Saham; Support Vector Machine (SVM); Moving Average; Data MiningAbstract
This study aims to analyze and predict stock price trends using the Support Vector Machine (SVM) method with the Moving Average indicator for PT Bukit Asam Tbk. The data used is historical stock price data consisting of open, high, low, adjusted close, volume, and technical moving average indicators (MA5 and MA10). The method used in this study is the Support Vector Machine algorithm implemented using the Weka application with a 10-fold cross-validation evaluation technique. The research process includes data collection, data preprocessing, calculation of moving average indicators, formation of trend variables, and the classification process using the SVM model. The test results indicate that the model is able to classify stock price trends with an accuracy level of 49.27%. This value indicates that the model still has limitations in optimally capturing patterns of stock price movement. Nevertheless, the use of the SVM method with the moving average indicator can be used as an approach in analyzing predictions of stock price trends. This study is expected to provide a reference for further research in developing models for predicting stock prices by adding other technical indicators or using different classification methods to increase the level of prediction accuracy.
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