Analisis Sentimen Terhadap Pariwisata di Masa Covid-19 Menggunakan Naïve Bayes
DOI:
https://doi.org/10.61769/telematika.v17i1.450Keywords:
Sentiment analysis, Covid-19, Naïve Bayes, tourism, text miningAbstract
The tourism industry is a sector that has the potential to be developed as a source of state income. The tourism sector is the second largest source of state revenue after taxes. The arrival of tourists in an area has an impact on local resident who have provided prosperity and prosperity in the vicinity. The Covid-19 pandemic that has occurred in the world has had a very broad impact, including on the tourism sector and the creative economy. The decline in foreign tourist arrivals resulted in huge losses. This creates a public response to government policies. The community's response to tourism can be seen in social media. One of the most popular social media is Twitter. Obtained as many as 3000 tweet data that will be classified using the Naïve Bayes algorithm. Naive Bayes is a text mining technique to build a simple classifier model but has high accuracy in classifying. With the use of the Naive Bayes algorithm in this study, the results obtained are 62% accuracy values with an average value of 62% precision, 62% recall value, and 62% F1-score value.
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