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All Journal IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Iza Rifna
Program Magister Teknologi Informasi, Universitas Malikussaleh, Aceh
Computer Science & IT Control & Systems Engineering

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Analisis sentimen pengguna tiktok terhadap program makan bergizi gratis menggunakan metode naive bayes Iza Rifna; Nurdin Nurdin
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 5 No 2 (2026): IT-Explore Juni 2026
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v5i2.2026.pp241-251

Abstract

The Free Nutritional Meal Program (MBG) is a government policy that is widely discussed by the public through social media, especially TikTok. Various comments that have emerged indicate differences in public opinion towards the program, so an analysis is needed to determine the tendency of public sentiment. This study aims to analyze TikTok user sentiment towards the Free Nutritional Meal Program using the Naive Bayes method. The research method is carried out through several steps, namely collecting TikTok comment data, preprocessing text, labeling sentiment data into positive, negative, and neutral, feature transformation using TF-IDF, and classification using the Naive Bayes algorithm. Based on the analysis of 500 comment data, the results show that positive sentiment dominates public opinion by 42% (210 data), followed by negative sentiment by 36% (180 data), and neutral sentiment by 22% (110 data). Testing the classification model using Naive Bayes produces excellent performance with an accuracy rate of 86%, precision of 84%, recall of 85%, and F1-score of 84%. The conclusion of this study shows that the Naive Bayes method is effective as an approach in social media sentiment analysis to map public responses to government policies.
Co-Authors Nurdin Nurdin