The Use of Naive Bayes Classifier in Sentiment Analysis at Indonesia's Super Priority Tourism Destinations Based on User Reviews

Abstract

This study aims to develop a digital technology-based evaluation platform to support Indonesia’s super priority tourism destination (DPSP) program. The platform utilizes tourist review data obtained from Google Maps, which is processed using sentiment analysis based on the Naïve Bayes algorithm and scraping techniques. By collecting and analyzing data in real time, this research provides accurate and relevant information about tourist opinions regarding tourism destinations. The implementation of this system is expected to enhance the effectiveness of tourism destination evaluations and support data-driven decision-making. The test result shows a model accuracy of 75%, with tourist reviews classified into positive, negative, and neutral classes. Further development is recommended to add more data sources and improve model accuracy.Penelitian ini bertujuan untuk mengembangkan platform evaluasi berbasis teknologi digital untuk mendukung Program Destinasi Pariwisata Super Prioritas (DPSP) di Indonesia. Platform ini memanfaatkan data ulasan wisatawan yang diperoleh dari Google Maps melalui teknik scraping dan diolah menggunakan analisis sentiment berbasis algoritma Naïve Bayes. Dengan mengumpulkan dan menganalisis data secara real-time, penelitian ini mampu memberikan informasi yang akurat dan relevan terkait opin