PENERAPAN ALGORITMA NAÏVE BAYES UNTUK KLASIFIKASI STATUS GIZI BALITA BERDASARKAN DATA ANTROPOMETRI DI POSYANDU

Hikmah Amelia Dewi Purwanti; Nining Rahaningsih; Irfan Ali; Edi Tohidi

Authors

Hikmah Amelia Dewi Purwanti STMIK IKMI Cirebon, Indonesia
Nining Rahaningsih STMIK IKMI Cirebon, Indonesia
Irfan Ali STMIK IKMI Cirebon, Indonesia
Edi Tohidi STMIK IKMI Cirebon, Indonesia

Keywords:

Naïve Bayes, Status Gizi Balita, Antropometri, Klasifikasi, Posyandu

Abstract

Pemantauan status gizi balita merupakan elemen penting dalam upaya peningkatan kualitas kesehatan masyarakat, terutama di tingkat layanan kesehatan dasar seperti Posyandu. Proses penilaian gizi yang selama ini dilakukan secara manual melalui pencatatan antropometri sering menghadapi berbagai kendala, seperti ketidakakuratan pengukuran, kesalahan pencatatan, serta variasi kemampuan kader dalam melakukan interpretasi data. Kondisi tersebut berpotensi menyebabkan kesalahan klasifikasi status gizi, sehingga intervensi kesehatan dapat terlambat atau tidak tepat sasaran. Penelitian ini bertujuan mengimplementasikan algoritma Naïve Bayes untuk mengklasifikasikan status gizi balita berdasarkan data antropometri yang meliputi usia, berat badan, tinggi badan, dan kategori gizi. Metode penelitian menggunakan pendekatan deskriptif kuantitatif dan eksperimen klasifikasi dengan memanfaatkan dataset antropometri balita dari Kaggle. Tahapan analisis meliputi pengumpulan data, pembersihan data, transformasi, pembagian data, pelatihan model, pengujian, serta evaluasi menggunakan akurasi, presisi, recall, F1-score, dan confusion matrix. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes mampu memberikan klasifikasi yang cepat dan stabil pada data antropometri, serta memiliki potensi besar untuk diterapkan sebagai alat bantu pengambilan keputusan di Posyandu. Model yang dikembangkan mampu mengurangi ketergantungan pada penilaian manual dan meningkatkan objektivitas klasifikasi status gizi balita. Penelitian ini menegaskan bahwa teknologi machine learning dapat mendukung sistem pemantauan gizi komunitas dan berkontribusi pada peningkatan efektivitas program kesehatan masyarakat, khususnya dalam deteksi dini risiko malnutrisi di tingkat Posyandu.

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