Analyzing Public Sentiment Toward Probolinggo UMKM Products on TikTok Through Naïve Bayes Classification and Data Visualization - Umi Diantika; Wahyu Nofiyan Hadi; Anisa Nurul Wilda; Moh. Fadel; Muhammad Ichsan; Rojil Ghufron; Syifa Ayu Mika Bahrul; Arya Dwi Nugraha; Sischa Wahyuning Tyas (International Journal of Computer, Network Security and Information System, 2026)
The development of social media, particularly TikTok, has become one of the main platforms for the public to provide opinions and reviews on MSME products. This research aims to analyze public sentiment towards MSME products in Probolinggo based on user comments on TikTok using the Naïve Bayes Classification method. Research data was obtained through scraping TikTok comments, followed by preprocessing steps including cleaning text, case folding, tokenizing, stopword removal, and stemming. The data was then labeled as positive, neutral, and negative sentiment before feature extraction using TF-IDF. The classification process was carried out using the Naïve Bayes algorithm combined with SMOTE on training data to overcome class imbalance, and evaluated using pure test data. The results showed that the Naïve Bayes method was able to classify sentiment with a final accuracy rate of 66.67%. The analysis showed that neutral sentiment dominated public opinion with 365 comments (47.04%), followed by positive sentiment with 341 comments (43.94%) and negative sentiment with 70 comments (9.02%). Data visualization indicated that most users responded very positively to MSME Probolinggo products on TikTok. This research is expected to help MSME actors understand consumer perceptions and improve digital marketing strategies based on social media