<![CDATA[The increasing gap between academic outcomes and labor market demands has led to a significant skills mismatch among Information Technology graduates. The purpose of this research is to develop a stacking ensemble-based decision-support framework for analyzing and predicting employability outcomes in a multidimensional skill context. The method used is a stacking ensemble learning approach, in which multiple base learners are combined and optimized with XGBoost as the meta-learner. The study uses a synthetic dataset of 2,000 records with 31 variables designed to represent realistic employability factors, including academic performance, technical skills, soft skills, certifications, and career preferences. To enhance interpretability, SHAP (Shapley Additive exPlanations) is employed to identify the contribution of each feature to the prediction outcomes. The result of this study isthat the proposed stacking framework achieves superior performance compared to individual models, demonstrating improved predictive accuracy and robustness. The analysis further reveals that GPA, technical competencies, soft skills, and professional certifications strongly influence employability. In conclusion, the proposed framework not only improves prediction performance but also provides interpretable insights that support decision-making. These findings offer practical implications for higher education institutions and policymakers in designing curriculum strategies and targeted training programs to reduce skills mismatch and enhance IT graduate employability.]]>
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