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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer)
Yudika Kristian Butar-butar
Universitas Prima Indonesia
Computer Science & IT

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PENERAPAN DATA MINING UNTUK KLASIFIKASI BERITA HOAX MENGGUNAKAN ALGORITMA NAIVE BAYES Saut P Tamba; Agusteti Laia; Yudika Kristian Butar-butar; Anita Anita
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.922

Abstract

The research aims to develop a classification model that is effective in identifying hoax news. The rapid development of information technology has had a significant impact on the dissemination of information, especially in the context of the spread of hoaxes via the internet. Hoaxes, or fake news, can cause misperceptions and negative impacts on various aspects of people's lives. To classify hoax news, this research was carried out using the Naive Bayes algorithm. The data used comes from various sources and involves stages of data collection, data analysis, and preprocessing processes. Modeling uses the Naive Bayes Algorithm, which applies the law of probability, to calculate the confidence or probability that a news item falls into the fraud category. The data preprocessing process includes tokenization, case transformation, stopwords filter, and tokens filter (by Length), which aims to improve the quality of the analyzed data. Model evaluation was carried out using cross-validation, confusion matrix, and classification report methods. The evaluation results show that the model accuracy is 73.91%, with a deviation of 1.04%. The results of this research can be used to classify hoax news properly. This model can be used as an initial reference in developing more complex prediction models.
Co-Authors Agusteti Laia Anita Anita Saut P Tamba