Klasifikasi Stunting Pada Balita Berdasarkan Status Gizi Menggunakan Pendekatan Support Vector Machine (SVM)
DOI:
https://doi.org/10.22441/fifo.2024.v16i2.007Keywords:
Klasifikasi Stunting, Pembelajaran Mesin, Status Gizi, Support Vector Machine, SVMAbstract
Stunting pada balita merupakan masalah gizi serius yang berdampak pada perkembangan fisik dan kognitif anak, terutama di negara berkembang seperti Indonesia. Dengan prevalensi stunting yang masih tinggi, identifikasi dini balita yang berisiko sangat penting untuk mencegah dampak jangka panjang. Namun, metode konvensional dalam mengidentifikasi stunting sering kali kurang akurat dan memerlukan banyak sumber daya. Tujuannya penelitian ini dilakukan yaitu untuk mengklasifikasikan stunting pada balita berdasarkan status gizi melalui pembelajaran mesin dengan algoritma Support Vector Machine (SVM). Pemilihan SVM didasarkan pada keunggulannya dalam mengolah data multidimensi yang rumit serta kapabilitasnya untuk mengoptimalkan pemisahan antar kelas data dengan memaksimalkan margin. Penelitian ini juga menerapkan berbagai teknik prapemrosesan data, seperti standarisasi fitur, pengkodean variabel kategorikal, dan penghapusan data duplikat, untuk memastikan performa optimal model. Hasil penelitian mengungkapkan bahwa model SVM yang dibangun memperoleh akurasi sebesar 98,37%, menandakan kinerja yang sangat baik dalam klasifikasi status gizi balita. Temuan ini mengindikasikan bahwa SVM memiliki potensi besar untuk diaplikasikan dalam mendukung pengambilan keputusan medis dan intervensi kesehatan masyarakat, terutama dalam konteks pemantauan dan pencegahan stunting pada balita.Downloads
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