Analisis Sentimen Terhadap Media Sosial Twitter dengan Kasus Kampanye Anti-Korupsi di Indonesia Menggunakan Naive Bayes
DOI:
https://doi.org/10.30865/mib.v8i2.7582Keywords:
Sentiment Analysis, Anti Corruption, Anti-Corruption Campaign, KPK, TwitterAbstract
In Indonesia, the issue of the Anti-Corruption Campaign is often discussed on Twitter, which has become an important platform for voicing public opinions and sentiments. Sentiment analysis of the Anti-Corruption Campaign can provide valuable insights for the government and anti-corruption institutions such as the Corruption Eradication Commission. The aim of this research is to analyze the sentiment felt by the public towards the Anti-Corruption Campaign in Indonesia using the Naïve Bayes method based on data from Twitter. Tweet data related to the Anti-Corruption Campaign in Indonesia is collected via the Twitter API, then preprocessed to clean, tokenize and remove stopwords. A dictionary of positive and negative sentiments is created based on tweet analysis, and the Naïve Bayes method is used to classify tweet sentiments into positive or negative. Method performance is evaluated using a confusion matrix. The research results show that most of the tweets related to the Anti-Corruption Campaign have positive sentiments, but there are also negative ones. This research provides an understanding of public perceptions of the Anti-Corruption Campaign in Indonesia through Twitter sentiment analysis, which can help in formulating anti-corruption policies and strategies in the future. Analysis shows that 58.64% of Twitter accounts have positive sentiment and 41.36% have negative sentiment. Evaluation of test data shows an accuracy level of 74%, a precision level of 79%, and a recall of 76%.
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