COMPARISON OF AIR QUALITY DATA ACCURATION USING DECISION TREE AND NEURAL NETWORK METHOD

Jurnal Ipteks Terapan

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Field Value
 
Title COMPARISON OF AIR QUALITY DATA ACCURATION USING DECISION TREE AND NEURAL NETWORK METHOD
 
Creator Izhari, Fahmi; Universitas Pembangunan Pancabudi Medan
Dhany, Hanna Willa; Universitas Pembangunan Pancabudi Medan
 
Subject
Decision Tree, Neural Network, Classification, Accuracy

 
Description In research conducted on the Neural Network classification model that has been tested has an accuracy of 82.04% with a classification error rate of 17.96%. Meanwhile, the Decision Tree classification model has an accuracy rate of 99.38 %% with a classification error rate of 0.62%. Based on the test results from the two classification models, it can be concluded that the success of the Decision Tree can be used as a reference to improve the performance of the classification model's accuracy compared to the Neural Network Backpropagation model.
 
Publisher LLDIKTI Wilayah X
 
Contributor
 
Date 2020-07-16
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion


 
Format application/pdf
 
Identifier http://ejournal.lldikti10.id/index.php/jit/article/view/5389
10.22216/jit.2020.v14i2.5389
 
Source Jurnal Ipteks Terapan; Vol 14, No 2 (2020): JIT; 123-127
Jurnal Ipteks Terapan; Vol 14, No 2 (2020): JIT; 123-127
2460-5611
1979-9292
10.22216/jit.2020.v14i2
 
Language eng
 
Relation http://ejournal.lldikti10.id/index.php/jit/article/view/5389/pdf1
10.22216/jit.2020.v14i2.5389
 
Coverage


 
Rights Copyright (c) 2020 Fahmi Izhari, Hanna Willa Dhany
http://creativecommons.org/licenses/by-sa/4.0
 

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