Analisis Sentimen Perbedaan Pendapat Netizen Indonesia Terhadap Penutupan Tiktok Shop Menggunakan Algoritma Naïve Bayes
DOI:
https://doi.org/10.30865/json.v5i2.7170Keywords:
Sentiment Analysis, Naïve Bayes algorithm, TikTok Shop, Fast Miner, IdentificationAbstract
This research uses the Naïve Bayes algorithm to analyze the sentiments of Indonesian netizens regarding the closure of the TikTok Shop. This research focuses on analyzing differences of opinion spread on social media platforms. Data obtained from social media such as Youtube, Tiktok, and Threads. There is data that will later be used in this research with a total of 1366 data. Then, there were 987 positive data and 379 negative data. After conducting research, results will be obtained with an accuracy of 86.97% in the first experiment which does not use the Split Data operator, and an accuracy of 89.23% in the second experiment which uses the Split Data operator. Then the results of this analysis reveal significant variations in sentiment among Indonesian netizens regarding the closure of the TikTok Shop. Some groups of netizens may express disappointment or disapproval while others may show support for the decision. The analysis also identified key factors influencing dissent, such as user experience, expectations of the platform and economic impact. Due to this, this research contributes to the field of sentiment analysis and natural language processing which applies splitting procedures so that netizen comment data on the platform can be classified.References
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