Please use this identifier to cite or link to this item: http://repository.ukrida.ac.id//handle/123456789/621
Title: Identification of fat-soluble vitamins deficiency using artificial neural network
Authors: Hayat, Cynthia
Keywords: deficiency early diagnose
fat-soluble vitamin deficiency;
neural network;
back-propagation
Issue Date: 5-Feb-2020
Publisher: Jurnal Teknologi dan Sistem Komputer - Universitas Diponegoro
Series/Report no.: 8;1
Abstract: The fat-soluble vitamins (A, D, E, K) deficiency remain frequent universally and may have consequential adverse resultants and causing slow appearance symptoms gradually and intensify over time. The vitamin deficiency detection requires an experienced physician to notice the symptoms and to review a blood test’s result (high-priced). This research aims to create an early detection system of fat-soluble vitamin deficiency using artificial neural network Back-propagation. The method was implemented by converting deficiency symptoms data into training data to be used to produce a weight of ANN and testing data. We employed Gradient Descent and Logsig as an activation function. The distribution of training data and test data was 71 and 30, respectively. The best architecture generated an accuracy of 95 % in a combination of parameters using 150 hidden layers, 10000 epoch, error target 0.0001, learning rate 0.25.
URI: http://repository.ukrida.ac.id//handle/123456789/621
ISSN: 2620-4002
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