KLASIFIKASI STATUS GIZI PADA BALITA DENGAN MEMPERTIMBANGAN FAKTOR SOSIAL EKONOMI KELUARGA MENGGUNAKAN ALGORITMA ARTIFICIAL NEURAL NETWORK (ANN) BACKPROPAGATION (STUDI KASUS DESA TEGALGONDO KARANGPLOSO MALANG)
| Main Author: | Mulyana, Fitria |
|---|---|
| Format: | Thesis NonPeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
, 2018
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| Subjects: | |
| Online Access: |
http://eprints.umm.ac.id/40045/1/PENDAHULUAN.pdf http://eprints.umm.ac.id/40045/2/BAB%20I.pdf http://eprints.umm.ac.id/40045/3/BAB%20II.pdf http://eprints.umm.ac.id/40045/4/BAB%20III.pdf http://eprints.umm.ac.id/40045/5/BAB%20IV.pdf http://eprints.umm.ac.id/40045/6/BAB%20V.pdf http://eprints.umm.ac.id/40045/ |
Daftar Isi:
- Nutritional status in infants can generally be determined based on the index weight for age (W / A) or in other words can be calculated by the method of anthropometry. Research aims to build a model of Artificial Neural Network,or often called Artificial neural networks using algorithm backpropagation so that it can megenali able to classify the patterns and nutritional status of infants in good nutrition or malnutrition. The variables used in the classification of nutritional status are gender, age (months), Weight (kg), Revenue parents, educational level mothers / caregivers, and points of knowledge of mothers / caregivers. The sample in this study is the nutrition data toddlers aged (0-60) in as many as 125. The more the number of hidden layer neurons and number of theiterations that is given the better the value of Accuracy, precision and recall in getting. Thus it can be concluded that the Artificial Neural Network (ANN) Backpropagatin able to recognize patterns as well and were able to classify the nutritional status of children.