Ensemble Learning Approach on Indonesian Fake News Classification

Herley Shaori Al-Ash, Mutia Fadhila Putri, Petrus Mursanto, Alhadi Bustamam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

The news is information about a recently changed situation or a recent event. Serving as popular media information the internet has the power spread the news not only real news but fake news as well. We propose an ensemble learning approach on Indonesian fake news in order to separate fake news from the real one and to tackle imbalanced data problem which we face on the given dataset. Our experiment result shows that random forest classifier as the ensemble classifier which obtained 0.98 f1-score is superior to multinomial naive bayes and support vector machine as non-ensemble classifiers which achieve 0.43 and 0.74 f1-score respectively across 660 evaluation documents. We also compare our result against other research that using the same data and our approach achieved better results.

Original languageEnglish
Title of host publicationICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences
Subtitle of host publicationAccelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728146102
DOIs
Publication statusPublished - Oct 2019
Event3rd International Conference on Informatics and Computational Sciences, ICICOS 2019 - Semarang, Indonesia
Duration: 29 Oct 201930 Oct 2019

Publication series

NameICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings

Conference

Conference3rd International Conference on Informatics and Computational Sciences, ICICOS 2019
Country/TerritoryIndonesia
CitySemarang
Period29/10/1930/10/19

Keywords

  • ensemble learning
  • fake news
  • multinomial naïve bayes
  • random forest
  • support vector machine

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