PREDIKSI KOMPOSISI BAHAN PER 100 GRAM MAKANAN PENGGANTI ASI (MPASI) MENGGUNAKAN EXTREME LEARNING MACHINE (ELM)

Wiranto Widotomo, . (2021) PREDIKSI KOMPOSISI BAHAN PER 100 GRAM MAKANAN PENGGANTI ASI (MPASI) MENGGUNAKAN EXTREME LEARNING MACHINE (ELM). Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Children under the age of two are a vulnerable group in terms of nutritional problems because it will determine the quality of life of the next child. Complementary food for breastfeeding is nutritional intake for toddlers aged 6-24 months apart from breast milk as a support for toddlers' needs. Malnutrition for toddlers can cause physical and mental growth disorders, reduce intelligence levels, and can even cause death. One way to improve infant nutrition problems is to predict the amount of nutritional content in a complementary food recipe using an extreme learning machine, so that it can adjust the nutritional needs of toddlers. This study uses primary data, namely the composition of the ingredients and the nutritional value contained in the beef porridge recipe. The architecture in this model uses 500 neurons in the hidden layer, the activation function of the Rectifier Linear Unit, and a random initial weight value with a range of -1 to 1. Based on this research design, the results of the tests that have been carried out show that the distribution of K = 4 data in the training process has the smallest MAPE value, which is 0.00000008630. While in the testing process, the distribution of K = 1 data has the smallest MAPE value, which is 0.768392942..

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil : 1710511063], [Pembimbing 1 : Didit Widyanto], [Pembimbing 2 : Mayanda Mega Santoni], [Penguji 1 : Iin Ernawati], [Penguji 2 : Nurul Chamidah]
Uncontrolled Keywords: MPASI, Prediction, Extreme Learning Machine
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Wiranto Widotomo
Date Deposited: 21 Dec 2021 07:48
Last Modified: 21 Dec 2021 07:48
URI: http://repository.upnvj.ac.id/id/eprint/11205

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