AI Explanation related Covid Hoax Detection Using Support Vector Machine and Logistics Regression Methods

Authors

  • Naufal Haritsah Luthfi Telkom University, Bandung
  • Agus Hartoyo Telkom University, Bandung

DOI:

https://doi.org/10.30865/mib.v7i1.5386

Keywords:

Detection, Explainable-AI, Hoax, Logistic Regression, Support Vector Machine, Tf-Idf, Word

Abstract

Hoax news about Covid is still circulating in society. Especially on social media, this phenomenon still occurs. The existence of this disinformation can cause divisions between communities. Currently, technology can classify hoax news and non-hoax news. But no system can see the reasons for a model to classify hoax news and non-hoax news. Therefore, in this study, a system was developed that can see words on a system that detects hoax and non-hoax news using the Support Vector Machine and Logistic Regression methods. Meanwhile, the Explainable AI method is Local Interpretable Model-agnostic Explanations (LIME). The test results show that the SVM and Logistic Regression methods have the highest accuracy of 91% and 95%. The words collected in the dataset are sufficient to differentiate between a hoax and non-hoax news. It was found that hoax news about Covid-19 has many words related to Covid-19, religion, politics, medical, and words that are not related to Covid-19. Among them are "lockdown", "masjid", "rezim", "ventilator", and "kiamat". Meanwhile, non-hoax news about Covid-19 has many words related to Covid-19, government, and medical. Among them are "protokol", "isolasi", "infeksi", "menteri", and "nakes".

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Published

2023-01-28

Issue

Vol. 7 No. 1 (2023): Januari 2023

Section

Articles

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