<![CDATA[Stock price movements are highly dynamic, requiring prediction approaches that are not only accurate but also easy for users to understand. This study focuses on the development of abased stock price prediction information system for Bank Syariah Indonesia Tbk (BRIS) using a time series forecasting approach. The data used consist of historical BRIS stock prices (open, high, low, close, and volume) obtained from Investing.com and processed through data cleaning, normalization, and preparation to meet time series modeling assumptions. The prediction models applied in this study are ARIMA and SARIMA, with parameter selection based on ACF and PACF analysis. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to determine the accuracy level of the predictions. The evaluation results indicate that the ARIMA model outperforms the SARIMA model, achieving an MAE of 0,1266, RMSE of 0,1519, while the SARIMA model records an MAE of 0,1811, RMSE of 0,1955. The best model was then integrated into a web-based information system using Flask and React.js, which provides visualization of prediction results through interactive charts and comparisons with actual data. The system displays stock price prediction results in the form of interactive charts alongside actual data comparisons, aiming to help users understand stock price trends and support more objective, data-driven investment decisions.]]>
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