Sentiment Classification of General Election 2024 News Titles on Detik.com Online Media Website Using Multinominal Naive Bayes Method

Nathanael Yudhistira Pradipta; Hari Soetanto

doi:10.35877/454RI.asci2754

Nathanael Yudhistira Pradipta Faculty of Information Technology, Budi Luhur University, Ciledug, Jakarta 12260, Indonesia (ID)
Hari Soetanto Faculty of Information Technology, Budi Luhur University, Ciledug, Jakarta 12260, Indonesia (ID)

https://doi.org/10.35877/454RI.asci2754

Keywords: 2024 Election; digital democracy; Multinominal Naïve Bayes; sentiment

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Abstract

In The 2024 elections will produce a variety of political tendencies in society, with different opinions regarding its implementation and conditions. The role of news headlines is very important in influencing speculation and public responses to certain topics or issues. This study investigates the sentiment conveyed in news headlines about the 2024 Election using the Multinomial Naïve Bayes approach. Data was gathered from Detik.com, an online media platform, utilizing search terms “Pemilu 2024” and “Pemilihan Umum 2024” through web scraping methods. The data preprocessing involved converting to lowercase, tokenization, punctuation removal, stopword elimination, normalization, and stemming. Training data comprised 90% while 10% was allocated for testing. Analysis showed an accuracy of 83.59% using CountVectorizer for data transformation. Beyond sentiment classification, the research also examines how political processes shape media narratives and influence public perception. The implications highlight the impact of online media sentiment on digital democracy dynamics, providing valuable insights for political practitioners, policymakers, and media scholars. This study is not only academically significant but also offers practical insights for enhancing public understanding of the electoral process.

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