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      "keywords": "Status Gizi Balita, Random Forest,  ADASYN, Machine Learning,\r\nKlasifikasi, Toddler Nutritional Status, Classification.",
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      "abstract": "Masalah gizi pada balita di Indonesia, termasuk di Desa Mlese, merupakan \r\ntantangan signifikan yang berdampak pada gangguan tumbuh kembang dan\r\npenurunan kualitas sumber daya manusia di masa depan. Evaluasi status gizi\r\nkonvensional di Desa Mlese saat ini masih memiliki keterbatasan administratif dan\r\nbelum optimal dalam memprediksi pola data antropometri yang kompleks,\r\nsehingga diperlukan metode yang lebih responsif dan akurat. Penelitian ini\r\nmenerapkan pendekatan machine learning dengan algoritma Random Forest untuk\r\nmengklasifikasikan status gizi balita melalui tahapan pengumpulan data sekunder\r\nperiode 2020–2024, pra-pemrosesan data, serta seleksi fitur menggunakan metode\r\nRandom Forest Importance, Chi-Square, dan Recursive Feature Elimination\r\n(RFE). Untuk mengatasi ketidakseimbangan data, penelitian ini membandingkan\r\nteknik SMOTE, SMOTE-TomekLinks, dan ADASYN, yang dioptimasi menggunakan\r\nhyperparameter tuning melalui Randomized Search Cross Validation. Hasil\r\npenelitian menunjukkan bahwa integrasi ADASYN dengan Random Forest\r\nmemberikan performa terbaik dengan akurasi 0,8757 dan F1-Score 0,6898. Sistem\r\nyang dikembangkan tidak hanya berfungsi sebagai media pencatatan data, tetapi\r\njuga menonjolkan analisis frekuensi kunjungan balita untuk membantu pemantauan\r\nkepatuhan kunjungan posyandu, menyediakan visualisasi data yang lebih analitik\r\nuntuk melihat tren pertumbuhan dan distribusi status gizi secara individu maupun\r\npopulasi, serta mendukung deteksi dini risiko masalah gizi berbasis machine\r\nlearning. Berdasarkan pengujian User Acceptance Test (UAT) dengan skor 88,17%,\r\nsistem ini dinyatakan sangat layak untuk membantu kader posyandu dan pihak\r\npuskesmas dalam melakukan pemantauan berkelanjutan serta perencanaan\r\nintervensi gizi yang lebih tepat sasaran.",
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      "title": "KLASIFIKASI STATUS GIZI BALITA MENGGUNAKAN\r\nALGORITMA RANDOM FOREST DI DESA MLESE",
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