Deteksi Stunting Balita Menggunakan Algoritma ID3

Authors

  • Yoga Firnanda Wicaksono Universitas Nusantara PGRI Kediri Indonesia
  • Ardi Sanjaya Universitas Nusantara PGRI Kediri Indonesia
  • Ahmad Bagus Setiawan Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/ynthba83

Abstract

Stunting merupakan masalah gizi kronis pada balita yang dapat memengaruhi pertumbuhan fisik, perkembangan kognitif, dan kualitas sumber daya manusia di masa depan. Proses deteksi stunting di Posyandu Kamboja Desa Kwaron masih dilakukan secara manual sehingga rentan terhadap kesalahan pencatatan dan keterlambatan analisis data. Penelitian ini bertujuan untuk membangun sistem deteksi stunting balita otomatis menggunakan algoritma Iterative Dichotomiser 3 (ID3) berbasis Decision Tree. Dataset yang digunakan berjumlah 223 data balita dengan atribut jenis kelamin, umur, tinggi badan, berat badan, lingkar kepala, dan Lingkar Lengan Atas (LiLa). Tahapan penelitian meliputi preprocessing data, pembagian data dengan rasio 80:20, proses pelatihan model, serta evaluasi menggunakan confusion matrix. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan status stunting ke dalam kategori normal, pendek, sangat pendek, dan tinggi dengan tingkat akurasi sebesar 73,33%. Sistem yang dibangun diharapkan dapat membantu kader posyandu dalam melakukan deteksi stunting secara lebih cepat, terstruktur, dan akurat

Keywords:

Decision Tree, ID3, klasifikasi, machine learning, stunting

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References

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Published

2026-07-20

Issue

Vol. 10 No. 1 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026

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How to Cite

Deteksi Stunting Balita Menggunakan Algoritma ID3. (2026). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 10(1), 309-316. https://doi.org/10.29407/ynthba83