IMPLEMENTASI METODE MULTINOMIAL NAIVE BAYES DALAM KLASIFIKASI JUDUL BERITA CLICKBAIT

Satria Mahendra, Dicky (2024) IMPLEMENTASI METODE MULTINOMIAL NAIVE BAYES DALAM KLASIFIKASI JUDUL BERITA CLICKBAIT. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

This study aims to classify news titles into clickbait and non-clickbait using the Multinomial Naive Bayes method. The data used comes from the CLICK-ID dataset: A Novel Dataset for Indonesian Clickbait Headlines. The research process involves the stages of data collection, preprocessing, feature extraction, model training, model evaluation, and result analysis. The test results show that the Multinomial Naive Bayes algorithm is able to produce a consistent accuracy rate of around 78%. Through optimization using Grid Search, there is no improvement in accuracy. Although there was no increase in accuracy, there was an improvement in the recall value for the non-clickbait class from 76% to 80%. The best parameter found was alpha of 0.15. Thus, the Multinomial Naive Bayes Algorithm can be effectively used in addressing the problem of clickbait headline classification, with the potential to contribute to clickbait prevention efforts in the future.

Item Type:

Thesis (Undergraduate)

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