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Artificial Neural Network Pada Industri Non Migas Sebagai Langkah Menuju Revolusi Industri 4.0

Main Authors: Parlina, Iin, Wanto, Anjar, Windarto, Agus Perdana
Format: Article info application/pdf eJournal
Bahasa: eng
Terbitan: Universitas Islam Sumatera Utara , 2019
Subjects:
Artificial Neural Networks
Predictions
Sensitivity Analysis
Backpropagation
Export Value
Online Access: https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/1682
https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/1682/pdf
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fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Artificial Neural Network Pada Industri Non Migas Sebagai Langkah Menuju Revolusi Industri 4.0</title><creator>Parlina, Iin</creator><creator>Wanto, Anjar</creator><creator>Windarto, Agus Perdana</creator><subject lang="en-US">Artificial Neural Networks; Predictions; Sensitivity Analysis; Backpropagation; Export Value</subject><description lang="en-US">The research conducted aims to make predictions with artificial neural metwork (backpopagation) and sensitivity analysis in the non-oil processing industry for the value of industrial exports. Data was obtained from the Badan Pusat Statistik (BPS) in collaboration with the Ministry of Industry of the Republic of Indonesia in the last 7 years (2011-2017). The process is carried out by dividing the data into 2 parts (training and testing) to obtain the best architectural model. The data processing uses the help of Matlab 6.0 software. Model selection is done by try and try to get the best architectural model. In this study using 7 architectural models (15-2-1; 15-5-1; 15-10-1; 15-15-1; 15-2-5-1; 15-5-10-1 and 15- 10-5-1) who have been trained and tested. By using the help of Matlab 6.0 software, the best architectural model is obtained 15-2-1 with an accuracy rate of 93%, epoch training = 189,881, MSE testing = 0.001167108 and MSE training = 0,000999622. The best architecture will be continued to predict the non-oil industry based on the most dominant export value using sensitivity analysis. From the architectural model a prediction of 5 out of 15 non-oil and gas industries contributes: Food &amp; Beverage Industry, Textile &amp; Apparel Industry, Basic Metal Industry, Rubber Industry, Rubber and Plastic Goods and Metal Goods Industry, Not Machines and Equipment , Computers, Electronics and Optics.</description><publisher lang="en-US">Universitas Islam Sumatera Utara</publisher><contributor lang="en-US"/><date>2019-09-19</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/1682</identifier><identifier>10.30743/infotekjar.v4i1.1682</identifier><source lang="en-US">InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan; Vol 4, No 1 (2019): InfoTekJar September; 155-160</source><source>2540-7600</source><source>2540-7597</source><source>10.30743/infotekjar.v4i1</source><language>eng</language><relation>https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/1682/pdf</relation><rights lang="en-US">Copyright (c) 2019 Iin Parlina, Anjar Wanto, Agus Perdana Windarto</rights><rights lang="en-US">https://creativecommons.org/licenses/by/4.0</rights><recordID>--jurnal.uisu.ac.id-index.php-index-oai:article-1682</recordID></dc>
language eng
format Journal:Article
Journal
Other:info:eu-repo/semantics/publishedVersion
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File:application/pdf
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Journal:eJournal
author Parlina, Iin
Wanto, Anjar
Windarto, Agus Perdana
title Artificial Neural Network Pada Industri Non Migas Sebagai Langkah Menuju Revolusi Industri 4.0
publisher Universitas Islam Sumatera Utara
publishDate 2019
topic Artificial Neural Networks
Predictions
Sensitivity Analysis
Backpropagation
Export Value
url https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/1682
https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/1682/pdf
contents The research conducted aims to make predictions with artificial neural metwork (backpopagation) and sensitivity analysis in the non-oil processing industry for the value of industrial exports. Data was obtained from the Badan Pusat Statistik (BPS) in collaboration with the Ministry of Industry of the Republic of Indonesia in the last 7 years (2011-2017). The process is carried out by dividing the data into 2 parts (training and testing) to obtain the best architectural model. The data processing uses the help of Matlab 6.0 software. Model selection is done by try and try to get the best architectural model. In this study using 7 architectural models (15-2-1; 15-5-1; 15-10-1; 15-15-1; 15-2-5-1; 15-5-10-1 and 15- 10-5-1) who have been trained and tested. By using the help of Matlab 6.0 software, the best architectural model is obtained 15-2-1 with an accuracy rate of 93%, epoch training = 189,881, MSE testing = 0.001167108 and MSE training = 0,000999622. The best architecture will be continued to predict the non-oil industry based on the most dominant export value using sensitivity analysis. From the architectural model a prediction of 5 out of 15 non-oil and gas industries contributes: Food & Beverage Industry, Textile & Apparel Industry, Basic Metal Industry, Rubber Industry, Rubber and Plastic Goods and Metal Goods Industry, Not Machines and Equipment , Computers, Electronics and Optics.
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library Universitas Islam Sumatera Utara
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collection InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan)
repository_id 4523
subject_area Computer Modeling and Simulation/Model dan Simulasi Komputer
Computer Communications Networks/Jaringan Komunikasi Komputer
Algorithms/Algoritma
Computer Security, Data Security/Keamanan Komputer, Keamanan Data
city KOTA MEDAN
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