Analisis Perbandingan Algoritma Machine Learning Untuk Prediksi Stunting Pada Anak

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Stunting pada balita merupakan salah satu masalah kesehatan yang signifikan di negara berkembang seperti Indonesia, yang disebabkan oleh kekurangan gizi kronis. Identifikasi dini stunting sangat penting untuk mengurangi dampaknya terhadap perkembangan anak. Penelitian ini bertujuan untuk membandingkan efektivitas metode decision tree Algoritma C4.5 dan deep learning dalam mengidentifikasi stunting pada balita dengan menggunakan perangkat lunak RapidMiner. Metode penelitian ini menggunakan pendekatan komparatif dengan dua model pembelajaran mesin, yakni Algoritma C4.5 dan deep learning, yang diterapkan pada data stunting. Hasil penelitian menunjukkan bahwa metode deep learning memiliki tingkat akurasi yang lebih tinggi dibandingkan dengan Algoritma C4.5 dalam mendeteksi stunting. Kesimpulannya, deep learning lebih efektif dalam mengidentifikasi stunting pada balita dibandingkan dengan Algoritma C4.5, sehingga direkomendasikan untuk digunakan dalam sistem pendeteksian stunting di Indonesia.

Keywords: pengerdilan, pohon keputusan, algoritma c4.5, pembelajaran mendalam, rapidminer

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https://doi.org/10.51158/tecnoscienza.v6i2.713

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