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All Journal UNP Journal of Statistics and Data Science
Latifah Jayatri Febiola
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Analisis Sentimen Masyarakat Terhadap Korupsi Berdasarkan Tweet Menggunakan Klasifikasi Naive Bayes Zulzila, Alivia; Latifah Jayatri Febiola; Dodi Vionanda
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/345

Abstract

Corruption is one of the big problems faced in Indonesia. The still high rate of corruption can damage the integrity of government, hamper economic growth, and reduce public trust in public institutions. Even though the government has made efforts to eradicate corruption, such as the formation of the Corruption Eradication Commission (KPK), these big challenges remain. Social media, especially Twitter, has become an important platform for people to voice opinions and criticize corruption issues. Sentiment analysis is used to detect opinions in the form of judgments, evaluations, attitudes and emotions of a person. The textual classification algorithm used in this research is Naive Bayes. This research aims to determine public sentiment towards corruption in Indonesia in positive, negative and neutral categories. This is done by data preprocessing, data labeling, and classification. The results of sentiment classification using the Naïve Bayes method obtained positive sentiment of 11, negative sentiment of 14, and neutral sentiment of 1485. So it can be concluded that Indonesian society tends to have neutral sentiments towards corruption that occurs in Indonesia
Co-Authors Dodi Vionanda Zulzila, Alivia