TELKOMNIKA Telecommunication, Computing, Electronics and Control
Comparative study of standalone classifier and ensemble classifier

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Comparative study of standalone classifier and ensemble classifier

Decision tree
Ensemble learning
Naïve Bayes
Sentiment analysis
Support vector machine

Ensemble learning is one of machine learning method that can solve
performance measurement problem. Standalone classifiers often show a poor
performance result, thus why combining them with ensemble methods can
improve their performance scores. Ensemble learning has several methods, in
this study, three methods of ensemble learning are compared with standalone
classifiers of support vector machine, Naïve Bayes, and decision tree.
bagging, AdaBoost, and voting are the ensemble methods that are combined
then compared to standalone classifiers. From 1670 dataset of twitter
mentions about tourist’s attraction, ensemble methods did not show a specific
improvement in accuracy and precision measurement since it generated the
same result as decision tree as standalone classifier. Bagging method showed
a significant development in recall, f-measure, and area under curve (AUC)
measurement. For overall performance, decision tree as standalone classifier
and decision tree with AdaBoost method have the highest score for accuracy
and precision measurements, meanwhile support vector machine with
bagging method has the highest score for recall, f-measure, and AUC.

Tri Okta Priasni, Teddy Oswari

http://journal.uad.ac.id/index.php/TELKOMNIKA

Mar 30, 2021

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