OPTIMASI PENINGKATAN AKURASI KLASIFIKASI STUNTING MENGGUNAKAN ALGORITHM SUPPORT VECTOR MACHINE

I Gusti Prahmana; Kristina Annatasia Br Sitepu; Adek Maulidya; Toni Prabowo

doi:10.46576/djtechno.v7i2.9391

OPTIMASI PENINGKATAN AKURASI KLASIFIKASI STUNTING MENGGUNAKAN ALGORITHM SUPPORT VECTOR MACHINE

I Gusti Prahmana, Kristina Annatasia Br Sitepu, Adek Maulidya, Toni Prabowo

Abstract

Stunting merupakan tantangan kesehatan masyarakat yang berdampak signifikan pada perkembangan fisik dan kognitif anak di masa depan. Penelitian ini bertujuan untuk mengoptimalkan akurasi klasifikasi status gizi balita menggunakan algoritma Support Vector Machine (SVM). Dataset yang digunakan terdiri dari 161 data sekunder dari Dinas Pengendalian Penduduk dan Keluarga Berencana (PPKB) Kota Binjai, mencakup atribut umur, tinggi badan, dan berat badan. Metodologi penelitian meliputi tahap praproses, seleksi fitur, serta optimasi model melalui penyetelan parameter kernel, nilai cost (C), dan gamma. Evaluasi kinerja dilakukan menggunakan confusion matrix dengan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model SVM yang dioptimasi berhasil mencapai tingkat akurasi sebesar 84,85%. Model ini menunjukkan performa yang sangat kuat dalam mengidentifikasi status gizi normal, meskipun masih terdapat kendala dalam mengklasifikasikan kategori kurus akibat ketidakseimbangan data. Secara keseluruhan, algoritma SVM terbukti efektif dalam memetakan risiko stunting secara dini. Temuan ini diharapkan dapat menjadi instrumen pendukung bagi otoritas kesehatan dalam merancang program intervensi yang lebih presisi dan tepat sasaran bagi masyarakat.

Keywords

Stunting, Support Vector Machine, Optimasi, Machine Learning, Akurasi.

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DOI: https://doi.org/10.46576/djtechno.v7i2.9391