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

https://doi.org/10.5614/sostek.itbj.2025.24.2.7

Authors

  • Ery Setiyawan Jullev Atmadji Information Technolgy Department, Politeknik Negeri Jember, Jember, Indonesia
  • Yuyun Wabula Research Center for Data and Information Science, National Research and Innovation Agenc, Jakarta, Indonesia
  • Hayuning Titi Karsanti Research Center for Data and Information Science, National Research and Innovation Agenc, Jakarta, Indonesia
  • Kristopher Kristopher Information Technolgy Department, Politeknik Negeri Jember, Jember, Indonesia

Keywords:

sociotechnology, sentiment analysis, super priority tourism destination, tourism evaluation, big data

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.

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Published

2025-07-21

Issue

Vol. 24 No. 2 (2025): JULY 2025

Section

Articles
Copyright & Licensing

Copyright (c) 2025 Ery Atmadji, Yuyun Wabula, Hayuning Titi Karsanti, Kristopher Kristopher

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