CD Skripsi

Perbandingan Klasifikasi Status Gizi Balita Di Indonesiamenggunakan Jaringan Syaraf Tiruan Backpropagation Dan Learning Vector Quantization

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Children under the age of five are the most vulnerable demographic in terms of nutrition and health within a community. The health of toddlers is widely assessed based on their nutritional status, determined through a comparison of weight and length/height measurements using Child Anthropometry standards. This study aims to predict the nutritional status of toddlers in cities and districts across Indonesia using the Backpropagation and Learning Vector Quantization algorithms. For this purpose, a dataset consisting of 514 cities and districts in Indonesia was analyzed and categorized into four classes of nutritional status. Using 80% of the data for training and 20% for testing, the classification accuracy of the Backpropagation algorithm with a 2-neuron hidden layer was 77.8%. Meanwhile, the Learning Vector Quantization algorithm, with a 9-size codebook, achieved a classification accuracy of 80.77%. This research sheds light on the importance of accurately predicting and addressing the nutritional status of toddlers, particularly in urban and rural areas in Indonesia. The findings from this study could contribute to targeted interventions aimed at improving the health and well-being of this vulnerable age group.

Keywords: Backpropagation, Learning Vector Quantization, Nutritional Status.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1903113257

Penerbit

Pekanbaru : Universitas Riau FMIPA Statistika., 2024

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1903113257

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

STATISTIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

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