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Suci Kurniawati
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
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KOMBINASI LEXICON-BASED DAN MULTINOMIAL NAЇVE BAYES CLASSIFIER DALAM ANALISIS SENTIMEN ARTIS SONG JOONG KI SEBAGAI BRAND AMBASSADOR SCARLETT WHITENING Suci Kurniawati; Suparti Suparti; Sugito Sugito; Hasbi Yasin
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.143-153

Abstract

Twitter as a social media platform can be utilized as a means of exchanging information about current events. The topic of K-Drama is often discussed by the public on Twitter and reaches trending topics, especially during the pandemic in Indonesia. The popularity of K-Drama in Indonesia has led to marketing strategies where actors are chosen as brand ambassadors. Song Joong Ki is one of the actors who has been chosen by Scarlett Whitening products to become their brand ambassador. The public expresses their responses on Twitter, and sentiment analysis is necessary to classify these responses as positive, neutral, or negative. The sentiment analysis combines the Lexicon-Based method and Multinomial Naїve Bayes Classifier. SentiWordNet is used in the Lexicon-Based classification method. The data preprocessing stage of this research includes cleansing, case folding, word normalization, tokenizing, filtering, and stemming. The combination of the Lexicon-Based method and Multinomial Naїve Bayes Classifier yielded an accuracy score of 81.50%. The words “jadi”, “brand”, and “ambassador” dominate the word cloud, indicating that the public extensively discusses the appointment of Song Joong Ki as the brand ambassador for Scarlett Whitening.  
Co-Authors Hasbi Yasin Sugito Sugito Suparti Suparti