Optimasi Naive Bayes dan Cosine Similarity Menggunakan Particle Swarm Optimization Pada Klasifikasi Hoax Berbahasa Indonesia

Authors

  • Arfan Yoga Aji Nugraha Universitas AMIKOM Yogyakarta, Yogyakarta
  • Ferian Fauzi Abdulloh Universitas AMIKOM Yogyakarta, Yogyakarta

DOI:

https://doi.org/10.30865/mib.v6i3.4170

Keywords:

Hoax, Text classification, Naive Bayes, Cosine Similarity, PSO

Abstract

The widespread circulation of hoax news in the information technology era is increasingly troubling, therefore in this era an algorithm to classify hoax news is necessary, in this study researchers focused on optimizing the accuracy of hoax news classification in text documents. The algorithm that will be used is Naive Bayes and cosine Similarity which previously has been applied with particle swarm optimization algorithm. In this study, it was concluded that after feature selection using PSO in the Naive Bayes algorithm the accuracy obtained increased from 0.91 to 0.93 while in the cosine similarity algorithm the accuracy increased from 0.62 to 0.73 after feature selection using PSO

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Published

2022-07-25

Issue

Vol. 6 No. 3 (2022): Juli 2022

Section

Articles

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