Indonesian Language Hoax News Classification Basedn on Naïve Bayes

Ari Sudrajat, Ratna Rizky Wulandari, Elvathna Syafwan

Abstract


Hoax news in Indonesia causes various problems, therefore it is necessary to classify whether a news is in the hoax category or is valid. Naive Bayes is an algorithm that can perform classification but has a weakness, namely the selection of attributes that can affect accuracy so that it needs to be optimized by giving weights to attributes using the TF-IDF method. Classification using Naive Bayes and using TF-IDF as attribute weighting on a dataset of 600 data resulted in 82% accuracy, 84% precision, and 89% recall. The suggestion put forward is that it is better to use a larger number of datasets in order to produce higher accuracy.

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References


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DOI: https://doi.org/10.33633/jais.v7i1.5985

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Journal of Applied Intelligent System (e-ISSN : 2502-9401p-ISSN : 2503-0493) is published by Department of Informatics Universitas Dian Nuswantoro Semarang and IndoCEISS.

  

 

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