<![CDATA[The rapid growth of stock investors in Indonesia has not been accompanied by adequate investment analysis skills, increasing the risk of irrational investment decisions. This study aims to develop STOXAREA, a hybrid stock recommendation decision support system integrating Simple Additive Weighting (SAW), Extreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP). The research employed the Research and Development (R&D) method with a quantitative approach. Research data were obtained from the Yahoo Finance API, including historical stock prices, company fundamental data, and investor risk profiles collected through questionnaires. XGBoost was utilized to generate AI Scores, while SAW was applied to rank stocks based on user preferences. SHAP was implemented to improve prediction transparency. The results show that STOXAREA can produce personalized stock recommendations to assist investors in making more rational investment decisions, demonstrating positive performance in backtesting validation, supported by a Virtual Trading feature for simulation.]]>
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