📅 29 April 2026
DOI: 10.35671/jmtt.v5i1.82
Sentiment Analysis in User Reviews of Tourist Attractions in East Nusa Tenggara Using Machine Learning Classification
Journal of Multimedia Trend and Technology
Universitas Amikom Purwokerto
📄 Abstract
This study aims to analyze user review sentiments for six tourist attractions in East Nusa Tenggara Province by utilizing a large amount of review data obtained from Google Maps. Data was collected through a scraping process using Serp API, followed by cleaning and text pre-processing to improve data quality. Sentiment labeling was performed automatically using the Indo-BERT model to obtain three sentiment classes: positive, negative, and neutral. Text feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, then classified using the baseline Support Vector Machine (SVM) model and the optimized SVM model with Grid-Search CV. The evaluation results showed that the baseline SVM model produced an accuracy of 83.87%, but showed an imbalance in performance between classes with a Macro F1-score of 0.4287. After parameter optimization using Grid-Search CV, the optimized SVM model produced an accuracy of 78.27% with an increase in the Macro F1-score value to 0.4818. This increase indicates an improvement in the model's ability to recognize minority sentiment classes despite a decrease in overall accuracy. Overall, the optimized SVM model provides more balanced and representative classification results in describing tourists' perceptions based on online reviews, so it can be used as a basis for sentiment analysis in the tourism sector.
🔖 Keywords
#Sentiment Analysis; Support Vector Machine; TF-IDF; Google Maps; Travel Reviews
ℹ️ Informasi Publikasi
Tanggal Publikasi
29 April 2026
Volume / Nomor / Tahun
Volume 5, Nomor 1, Tahun 2026
📝 HOW TO CITE
Agustina, Aulia Dian; Arsi, Primandani; Subarkah, Pungkas; Santiko, Irfan, "Sentiment Analysis in User Reviews of Tourist Attractions in East Nusa Tenggara Using Machine Learning Classification," Journal of Multimedia Trend and Technology, vol. 5, no. 1, Apr. 2026.
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