Analisis Sentimen Terhadap Kejaksaan Dalam Penanganan Kasus Korupsi Pada Media Sosial Twitter Dengan Metode Naive Bayes Classification
As one of the authorized institutions responsible for handling corruption cases, the Attorney General's Office often receives attention or sentiments from the public, whether they are positive or negative sentiments. One way to gauge public sentiment is by conducting sentiment analysis on the social media platform Twitter. This research aims to create a machine learning model using the Naïve Bayes Classification algorithm based on Twitter data to determine the public's sentiment towards the Attorney General's Office in handling corruption cases. The classification model yielded accuracy, recall, precision, and f-measure scores of 89.67%, 91.39%, 88%, and 89.66%, respectively. From the sentiment analysis results, it can be concluded that the public expresses positive sentiment towards the Attorney General's Office in handling corruption cases during the period from January 2022 to December 2022, with a percentage of 53.14% for positive sentiment and 46.86% for negative sentiment.
Keywords: Attorney General's Office, Corruption, Naïve Bayes Classification, Sentiment Analysis, Twitter.
Bibliography: 38 References 2002 - 2022
Keywords: Attorney General's Office, Corruption, Naïve Bayes Classification, Sentiment Analysis, Twitter.
Bibliography: 38 References 2002 - 2022
Yuda Hermawan - Personal Name
120103004 - Yuda Hermawan
Skripsi TI
Indonesia
Universitas Paramadina
2023
Jakarta
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