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      "abstract": "Stunting merupakan gangguan pertumbuhan kronis pada balita akibat\r\nkurang gizi berkepanjangan yang berdampak serius terhadap kemampuan kognitif\r\ndan produktivitas anak di masa depan. Masalah ini seringkali terlambat dideteksi\r\nkarena keterbatasan alat bantu diagnosis yang presisi, sehingga diperlukan\r\npendekatan teknologi cerdas untuk membantu deteksi dini status gizi balita secara\r\ncepat dan akurat.\r\nPenelitian ini menerapkan metode Ensemble Learning untuk\r\nmengklasifikasikan status stunting secara biner menggunakan lima algoritma, yaitu\r\nDecision Tree sebagai baseline, Random Forest, XGBoost, LightGBM, dan\r\nCatBoost. Evaluasi kinerja dilakukan menggunakan Stratified 10-Fold CrossValidation\r\npada\r\n39.425\r\ndata\r\nbersih\r\ndengan\r\natribut\r\njenis\r\nkelamin,\r\numur,\r\ndan\r\ntinggi\r\n\r\nbadan.\r\n\r\nHasil\r\n\r\npenelitian menunjukkan bahwa seluruh algoritma memperoleh\r\nperforma sangat tinggi: Decision Tree 99,62%, Random Forest 99,72%, XGBoost\r\n99,67%, LightGBM 99,59%, dan CatBoost 99,67% (akurasi dan F1-Score).\r\nRandom Forest menempati peringkat pertama berdasarkan weighted F1-Score,\r\nnamun uji McNemar's test menunjukkan bahwa perbedaan antarmodel tidak\r\nsignifikan secara statistik (α = 0,05).\r\nTinggi badan dan umur terbukti sebagai indikator dominan pada seluruh\r\nmodel. Hasil penelitian ini dapat dimanfaatkan oleh instansi kesehatan, Puskesmas,\r\ndan kader Posyandu sebagai dasar sistem pendukung keputusan klinis untuk\r\nscreening awal stunting secara massal dan efisien.",
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