<![CDATA[Digital transformation has encouraged organizations to adopt Human Resource Analytics and Artificial Intelligence to support data-driven decision-making, including employee attrition prediction. Although numerous predictive models have been developed, most of them still suffer from limited interpretability, making their predictions difficult for Human Resource practitioners to understand and utilize. This study aims to develop an Explainable Predictive Analytics model for employee attrition prediction by integrating Information Gain, Random Forest, RandomizedSearchCV, and SHapley Additive exPlanations (SHAP). The study employs the IBM HR Analytics Employee Attrition & Performance dataset consisting of 1,470 employee records. The research workflow includes data preprocessing, feature selection using Information Gain, Random Forest model development, hyperparameter optimization using RandomizedSearchCV, model evaluation using Accuracy, Precision, Recall, F1-Score, and ROC-AUC, followed by model interpretation through SHAP Summary Plot and SHAP Feature Importance. The experimental results indicate that the model achieved 82.54% Accuracy, 37.50% Precision, 12.68% Recall, 18.95% F1-Score, and a ROC-AUC of 0.7439. Feature selection results indicate that OverTime has the highest Information Gain value, while MonthlyIncome is identified as the most influential feature according to Random Forest Feature Importance. The main contribution of this study is the integration of Information Gain-based feature selection, Random Forest optimization, and SHAP-based Explainable Artificial Intelligence into a unified Explainable Predictive Analytics framework, providing a more transparent and interpretable predictive model to support decision-making in Human Resource Management]]>
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