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All Journal JAIS (Journal of Applied Intelligent System) Jurnal Ipteks Terapan : research of applied science and education Tedc
Elvathna Syafwan
Politeknik TEDC Bandung
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Journal : JAIS (Journal of Applied Intelligent System)

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Indonesian Language Hoax News Classification Basedn on Naïve Bayes Ari Sudrajat; Ratna Rizky Wulandari; Elvathna Syafwan
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5985

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.
Co-Authors Agus Saleh Ari Sudrajat Efendi Efendi Muhammad Quraish Jumaati Narwen Narwen Ratna Rizky Wulandari Rizki Ramdani Syafwan, Mahdhivan