Nutritional Status Classification of Children Using a Decision Tree Method (Case Study at RS AN-NISA) Based on a Web Application

Michael Julius Hutabarat; Dwi Sartika Simatupang; Masmur Tarigan; Y Yulhendri

doi:10.32664/smatika.v16i01.2238

Penulis

Michael Julius Hutabarat Universitas Esa Unggul
Dwi Sartika Simatupang, S.T., M.T.I Universitas Esa Unggul
Dr. Masmur Tarigan, S.T., M.Kom Universitas Esa Unggul
Yulhendri , ST, M.T Universitas Esa Unggul

DOI:

https://doi.org/10.32664/smatika.v16i01.2238

Kata Kunci:

Gizi, Decision Tree, Machine Learning, Klasifikasi, Flask

Abstrak

Penelitian ini mengembangkan model klasifikasi status gizi anak usia 0–5 tahun berbasis web menggunakan algoritma Decision Tree (C4.5) pada data antropometri RS AN-NISA Tangerang periode Januari–Desember 2024. Atribut yang digunakan meliputi umur (bulan), jenis kelamin, berat badan, dan tinggi badan, dengan label status gizi yang diklasifikasikan ke dalam kategori Gizi Kurang, Gizi Baik, dan Gizi Lebih. Data diproses melalui tahapan preprocessing yang mencakup pembersihan data kosong dan nilai ekstrem, seleksi atribut, encoding data kategorikal, normalisasi, serta pembagian data sebesar 80% data latih dan 20% data uji. Model dibangun menggunakan kriteria entropy dengan pengaturan hyperparameter untuk mengurangi risiko overfitting, kemudian dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score, serta dibandingkan dengan model baseline Logistic Regression. Hasil evaluasi menunjukkan bahwa Decision Tree mencapai nilai akurasi sebesar 96,12% dan recall macro 89,49%, yang lebih unggul dibandingkan Logistic Regression. Selanjutnya, model diserialisasi dan diintegrasikan ke dalam aplikasi berbasis Flask untuk memfasilitasi input data dan menghasilkan prediksi status gizi secara langsung. Hasil pengujian black box dan User Acceptance Test (UAT) menunjukkan tingkat kepuasan pengguna sebesar 88%, sehingga sistem dinilai layak digunakan sebagai alat bantu deteksi dini status gizi anak pada layanan kesehatan sehari-hari.

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