<![CDATA[Predicting stock prices is complex due to the non-linear and volatile characteristics of financial time-series data. This study presents a comparative analysis and web-based implementation of Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU) architectures to predict the daily closing prices of PT Bank Central Asia Tbk (BBCA.JK). Using Yahoo Finance historical data from January 1, 2018, to December 31, 2024 (1,725 trading days) via a univariate approach, the dataset was partitioned into 70% training, 15% validation, and 15% testing. Empirical results demonstrate that the GRU model significantly outperforms Bi-LSTM, achieving a Root Mean Squared Error (RMSE) of 145.63, a Mean Absolute Error (MAE) of 116.95, and a Mean Absolute Percentage Error (MAPE) of 1.23%. A Paired ttest confirmed the statistical significance of this superiority, yielding a t-statistic of ???? = -4,821 and a pvalue of ???? = 0,00012 ( ???? < 0,05). The GRU model was integrated into a microservices web application using Flask API and Laravel, incorporating a rule-based Decision Support System (DSS) to provide operational trading signals (Buy, Sell, Hold) along with Take Profit and Stop Loss calculations. Evaluation via User Acceptance Testing with 21 respondents yielded a feasibility score of 82.43% ("Highly Feasible")]]>
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