Authors - Francka Sakti Lee, Christian Haposan Pangaribuan, Liem Bambang Sugiyanto, Sulistyowati, Jovann Kurniawan, Henry Nugraha Abstract - Voluntary employee attrition presents a systemic challenge to organizational stability, yet predictive modeling is frequently constrained by the accuracy paradox and algorithmic opacity. This study proposes an Explainable Artificial Intelligence (XAI) framework integrating eXtreme Gradient Boosting (XGBoost) with Shapley Additive exPlanations (SHAP) to transform attrition analysis into prescriptive intelligence. By implementing a Random Over-Sampling (ROS) protocol, the model successfully neutralized extreme class imbalances, significantly enhancing the detection sensitivity of latent resignation signals. The novelty of this research lies in its SHAP-driven demographic bifurcation, exposing critical asymmetries in risk triggers between young and senior employees. Empirical findings identify a "Burnout Triad" comprising compensation, overtime, and job satisfaction. Crucially, junior cohorts exhibit hypersensitivity to immediate transactional factors, whereas senior cohorts are driven by intrinsic role actualization. This framework culminates in a Decision Support System (DSS) enabling surgical retention interventions, shifting human capital management toward strategic, evidence-based governance.
Open Zoom