DOI: https://doi.org/10.14710/jtsiskom.8.1.2020.6-11

Identification of fat-soluble vitamins deficiency using artificial neural network

Department of Information System, Faculty of Computer Science and Engineering, Krida Wacana Christian University, Indonesia

Received: 29 Apr 2019; Revised: 14 Oct 2019; Accepted: 17 Oct 2019; Available online: 5 Nov 2019; Published: 31 Jan 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to cite (IEEE): N. Sagala, C. Hayat, and F. Tandipuang, "Identification of fat-soluble vitamins deficiency using artificial neural network," Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 1, pp. 6-11, Jan. 2020. https://doi.org/10.14710/jtsiskom.8.1.2020.6-11
Citation Format:
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.
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Keywords: deficiency early diagnose; fat-soluble vitamin deficiency; neural network; back-propagation
Funding: Krida Wacana Christian University

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Section: Original Research Articles
Language : EN
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