KOMPARASI ALGORITMA KLASIFIKASI NAIVE BAYES DAN K-NEAREST NEIGHBORS DALAM ANALISIS SENTIMEN TERHADAP OPINI FILM PADA TWITTER
DOI:
https://doi.org/10.55606/jitek.v3i1.1147Keywords:
Data Mining, komparasi, Naive Bayes, k-nearest neigbors, analisis sentimen, twitterAbstract
The fact that social media is so unreliable does not prevent Twitter users from using the service. Twitter is one of a few social media platforms that allows users to engage in conversation, share information, or even reveal their true identities, such as when discussing a movie's plot. A tweet or comment about a movie that is posted on Twitter may be viewed as a tool to improve the quality of movie production. To understand this, one can use sentimen analysis to categorize as either negative or positive by comparing the Naive Bayes and k-Nearest Neighbors algorithms to determine which one is the most accurate. The results of the two algorithms' comparative testing reveal that the Nave Bayes algorithm has a higher rata-rata accuracy of 99.63% with an AUC of around 1.000, while the K-NN algorithm has a higher rata-rata accuracy of 99.25% with an AUC of 1.000.
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