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Mu'asyir, Humam
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Perbandingan Metode Naïve Bayes dan Support Vector Machine untuk Klasifikasi Sentimen Ulasan Wisatawan: Studi Kasus Mulia Resort Nusa Dua Bali Setiawan, Kiki; Mu'asyir, Humam
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 2 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i2.5416

Abstract

The tourism industry requires systems that efficiently capture tourist perceptions. Online reviews on platforms like TripAdvisor provide valuable insights but are challenging to analyze manually due to their volume and diversity. This study develops a sentiment classification model for tourist reviews by comparing Naïve Bayes and Support Vector Machine (SVM). The dataset comprises public reviews of Mulia Resort Nusa Dua Bali, categorized as positive or negative. Text preprocessing includes tokenization, stopword removal, and TF-IDF transformation. Model performance is evaluated using accuracy, precision, recall, and F1-score. The study delivers a ready-to-use sentiment classification model and comparative performance analysis of both algorithms. Findings are expected to identify the more effective method for sentiment analysis of tourist reviews and provide a reference for building recommendation systems and strategic decision-making in the tourism sector.
Co-Authors Setiawan, Kiki