Machine Learning Untuk Klasifikasi Gizi Balita Menggunakan Algoritma Random Forest
DOI:
https://doi.org/10.22441/incomtech.v15i2.30517Kata Kunci:
Stunting, Random Forest, Gizi Balita, Klasifikasi,Abstrak
Kesehatan balita merupakan isu kritis dalam pembangunan suatu negara. Penilaian status gizi balita adalah langkah awal untuk mengidentifikasi risiko malnutrisi dan memberikan intervensi yang tepat. Dalam penelitian ini, kami mengusulkan sebuah pendekatan inovatif menggunakan teknik Machine Learning, khususnya algoritma Random Forest, untuk klasifikasi status gizi balita berdasarkan karakteristik demografis dan pola makan. Dataset yang digunakan terdiri dari informasi demografis seperti usia, jenis kelamin, berat badan, tinggi badan, dan data gizi pada setiap balita. Algoritma Random Forest dipilih karena kemampuannya dalam mengatasi overfitting, mengelola data yang tidak seimbang, dan memberikan hasil klasifikasi yang akurat. Berdasarkan penelitian yang telah dilakukan dapat ditarik kesimpulan Tingkat akurasi yang dihasilkan dari algoritma random forest sebesar 83% dari 168 sampel menunjukkan bahwa model klasifikasi yang digunakan memberikan prediksi yang sempurna atau benar untuk seluruh data uji yang digunakan.
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