Stacking Ensemble Learning untuk Klasifikasi Status Gizi Balita Berdasarkan Data Antropometri: Studi Komparatif dan Validasi Lintas Dataset
DOI:
https://doi.org/10.22303/infosys.10.2.2026.113-130Keywords:
stunting, klasifikasi status gizi, stacking ensemble, machine learning, validasi lintas datasetAbstract
Stunting merupakan kondisi kegagalan pertumbuhan linear pada balita akibat akumulasi kekurangan gizi kronis, yang di Indonesia masih menjadi masalah kesehatan masyarakat dengan prevalensi nasional sebesar 19,8% pada tahun 2024. Penelitian ini bertujuan membangun model stacking ensemble untuk klasifikasi status gizi balita berdasarkan data antropometri (usia, jenis kelamin, dan tinggi badan), serta mengevaluasi kemampuan generalisasi model melalui validasi lintas dataset. Metode yang digunakan adalah stacking ensemble dengan Random Forest, Support Vector Machine, dan XGBoost sebagai base learner, serta Logistic Regression sebagai meta-learner. Dua dataset publik digunakan, yaitu dataset A dari Kaggle (120.999 balita) dan dataset B dari Mendeley Data yang bersumber dari Kabupaten Jeneponto, Sulawesi Selatan (39.853 balita setelah pembersihan). Hasil pengujian dalam-dataset pada dataset A menunjukkan model stacking ensemble memperoleh akurasi 99,94% dan F1-score makro 99,90%, sedikit mengungguli Random Forest (99,93%), XGBoost (99,12%), dan SVM (98,37%). Namun, hasil validasi lintas dataset menunjukkan penurunan performa yang signifikan, dengan akurasi 68,80% saat model dilatih pada dataset A dan diuji pada dataset B, serta 79,12% pada arah sebaliknya. Temuan ini mengindikasikan bahwa performa tinggi pada pengujian dalam-dataset belum tentu mencerminkan kemampuan generalisasi model terhadap populasi atau sumber data yang berbeda. Skema validasi lintas dataset yang diterapkan dalam penelitian ini mengungkap adanya kesenjangan generalisasi (generalization gap) yang belum banyak dibahas pada studi klasifikasi status gizi balita sebelumnya, sehingga temuan ini dapat menjadi dasar bagi pengembangan model prediksi stunting yang lebih andal di masa mendatang.
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