<![CDATA[Stunting remains a nutritional problem in children that is difficult to detect early due to its multifactorial causes and the limitations of conventional analytical approaches. This study aims to develop a stacking-based Machine Learning model for stunting prediction and to implement Explainable Artificial Intelligence (XAI) to interpret the risk factors influencing the prediction outcomes. The methods employed include Decision Tree, Random Forest, and Support Vector Machine (SVM) as base learners, and Logistic Regression as the meta-learner in an ensemble stacking framework, with performance evaluated using accuracy, precision, recall, and F1-score. The results indicate that the Decision Tree algorithm and the Stacking method achieved the best performance with 100% accuracy, while the XAI analysis identified current body weight, birth weight, and age as the primary factors influencing stunting prediction.Keywords: Stunting; Stacking; Machine Learning; Explainable Artificial Intelligence AbstrakStunting masih menjadi permasalahan gizi pada anak yang sulit dideteksi secara dini karena dipengaruhi oleh berbagai faktor dan keterbatasan analisis konvensional. Penelitian ini bertujuan mengembangkan model stacking berbasis Machine Learning untuk prediksi stunting serta menerapkan Explainable Artificial Intelligence (XAI) dalam menginterpretasikan faktor-faktor risiko yang berpengaruh terhadap hasil prediksi. Metode yang digunakan meliputi algoritma Decision Tree, Random Forest, Support Vector Machine (SVM) sebagai base learner dan Logistic Regression sebagai meta learner pada ensemble stacking, dengan evaluasi menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Decision Tree dan penerapan metode Stacking menghasilkan kinerja terbaik dengan tingkat akurasi 100%, sementara analisis XAI mengidentifikasi bahwa berat badan, BB lahir, dan usia merupakan faktor utama yang memengaruhi prediksi stunting.Kata kunci: Stunting; Stacking; Machine Learning; Explainable Artificial Intelligence]]>
