Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media

Nova Agustina; Adrian Adrian; Mercy Hermawati

doi:10.30998/faktorexacta.v14i4.11259

Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media

Nova Agustina^(1*), Adrian Adrian⁽²⁾, Mercy Hermawati⁽³⁾

(1) Sekolah Tinggi Teknologi Bandung
(2) Sekolah Tinggi Ilmu Ekonomi Jayakarta
(3) Universitas Indraprasta PGRI
(*) Corresponding Author

Abstract

Hoax news (lie) on the internet has become a global problem that causes turmoil in society. Its presence can disrupt democratic order, the stability of social, cultural, political and economic life. The results of the research of the Indonesian Telematics Society showed that as many as 44.3% of respondents said they received fake news or misinformation every day. According to information released by Kominfo until August 11, 2021, there were 1848 hoax reports regarding the Covid-19 pandemic, 290 hoax reports regarding the Covid-19 Vaccine. Naïve Bayes Classifier is a classification method based on Bayes theorem, which in this paper is used to detect fake news on social media. The analysis was carried out using the Naïve Bayes Classifier algorithm, in this study using the CRISP-DM (Cross-Industry Standard Process for Data Mining) model. Training data sourced from the Kumparan site as much as 300 Data. In the process carried out using the python library for NLP, namely "satrawi". In testing the model using the confusion matrix method which consists of the number of rows of test data that are predicted to be true and false by the classification model used. At the deployment stage the model is pushed to Heroku so that users can predict news directly through the provided User Interface.

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DOI: http://dx.doi.org/10.30998/faktorexacta.v14i4.11259