Perbandingan Metode Naive Bayes Classifier dan Support Vector Machine Pada Analisis Sentimen Wisata Biru Berdasarkan Ulasan Twitter, Instagram, dan Google Maps Review
- Selvi Rahmadila Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
-
Debby Alita
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
(*) Corresponding Author
Abstract
Blue tourism in Lampung Province has been recognized as a leading regional asset encompassing coastal areas, islands, and marine zones with strong appeal to visitors. Public responses toward these destinations can be captured through online reviews distributed across multiple digital platforms. In this study, the performance of sentiment classification algorithms, namely Naive Bayes Classifier and Support Vector Machine, was examined and compared using reviews related to blue tourism in Lampung. A total of 3,950 review records were collected from Twitter or X, Instagram, and Google Maps Review. The collected data were subjected to a series of preprocessing stages, including text cleaning to remove irrelevant elements, followed by theme and sentiment labeling using a semi supervised learning approach. Feature representation was generated through the Term Frequency Inverse Document Frequency method to transform textual data into numerical form. The labeling results revealed an imbalanced sentiment distribution with a strong dominance of positive sentiment. Model evaluation was conducted using an 80 to 20 split between training and testing datasets. The evaluation results indicated that the Support Vector Machine achieved an accuracy of 91.90 percent, while the Naive Bayes Classifier reached an accuracy of 90.38 percent. These findings suggest that the Support Vector Machine demonstrates superior capability in handling high dimensional textual data and imbalanced sentiment distributions. The outcomes of this study are expected to provide empirical guidance in selecting appropriate sentiment analysis algorithms to support data driven management and development of blue tourism destinations.
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