MODEL JARINGAN SYARAF TIRUAN MEMPREDIKSI EKSPOR MINYAK SAWIT MENURUT NEGARA TUJUAN UTAMA

Jurnal Teknovasi : Jurnal Teknik dan Inovasi

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Title MODEL JARINGAN SYARAF TIRUAN MEMPREDIKSI EKSPOR MINYAK SAWIT MENURUT NEGARA TUJUAN UTAMA
 
Creator Saifullah, Saifullah
Hidayati, Nani
Solikhun, Solikhun
 
Subject Neural Network
Palm Oil, Export, Prediction, Backpropagation, Artificial Neural Networks
 
Description This study aims to find the best architectural model in predicting palm oil exports according to the main destination countries. The role of the agricultural sector in the national economy is very important and strategic. Oil Palm is an industrial plant producing cooking oil, industrial oil, and bio-diesel fuel. Indonesia is the largest producer and exporter of palm oil in the world. In addition to the increasingly open export opportunities, the domestic market for palm oil and palm kernel oil is still quite large. Prediction is a process for estimating how many needs in the future. State revenues in the export sector must be able to be predicted to help set the state's financial regulations specifically on palm oil exports. By using Artificial Neural Networks and backpropagation algorithms, architectural models will be sought to predict the amount of palm oil exports according to the main destination country. This study uses 12 input variables, and 1 hidden layer. Using 4 architectural models to test the data to be used for prediction, namely models 12-4-1, 12-8-1, 12-16-1 and 12-32-1. The results of the best architectural model are architectural models 12-16-1 with 100% accuracy accuracy.
 
Publisher LPPM Politeknik LP3I Medan
 
Contributor
 
Date 2019-10-08
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Artikel yang dipeer-review
 
Format application/pdf
 
Identifier http://ejurnal.plm.ac.id/index.php/Teknovasi/article/view/306
 
Source Jurnal Teknovasi : Jurnal Teknik dan Inovasi; Vol 6, No 2 (2019): TEKNOVASI OKTOBER 2019; 85-95
Jurnal Teknovasi : Jurnal Teknik dan Inovasi; Vol 6, No 2 (2019): TEKNOVASI OKTOBER 2019; 85-95
2540-8389
2355-701X
 
Language ind
 
Relation http://ejurnal.plm.ac.id/index.php/Teknovasi/article/view/306/pdf
http://ejurnal.plm.ac.id/index.php/Teknovasi/article/downloadSuppFile/306/29
 
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