Klasifikasi Minat Belanja Online pada TikTok Berdasarkan Konten Media Sosial Menggunakan Metode Naive Bayes
DOI:
https://doi.org/10.47065/jimat.v6i1.967Keywords:
Tiktok; Online Shopping Interest; Social Media Content; Data Mining; Naive BayesAbstract
The development of social media, especially TikTok, has driven changes in consumer behavior in online shopping through digital content and the TikTok Shop feature. This study aims to classify TikTok users' online shopping interests based on the type of social media content using the Naïve Bayes method. This study uses a quantitative approach with data mining methods on 100 data consisting of 50 data (50%) in the Interested category and 50 data (50%) in the Not Interested category. The types of content analyzed include Promotion, Product Review, Entertainment, Education, and Vlog. The results of the Naïve Bayes implementation show a prior probability value of P(Interested) = 0.5 and P(Not Interested) = 0.5. The conditional probability calculation produces a posterior value of 0.25 for Promotion and Product Review content, so they are classified as Interested. Conversely, Entertainment and Education content has a posterior value of 0.19, while Vlog content is 0.12, so it is classified as Not Interested. The final results showed that 2 of 5 content types (40%) influenced shopping interest, while 3 content types (60%) had no effect. Thus, the Naïve Bayes method is effective for classifying online shopping interest based on TikTok content type.
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