Sentiment Analysis in User Reviews of Tourist Attractions in East Nusa Tenggara Using Machine Learning Classification

Aulia Dian Agustina; Primandani Arsi; Pungkas Subarkah; Irfan Santiko

doi:10.35671/jmtt.v5i1.82

Authors

Aulia Dian Agustina Universitas Amikom Purwokerto
Primandani Arsi Universitas Amikom Purwokerto
Pungkas Subarkah Universitas Amikom Purwokerto
Irfan Santiko Universitas Amikom Purwokerto

DOI:

https://doi.org/10.35671/jmtt.v5i1.82

Keywords:

Sentiment Analysis, Support Vector Machine, TF-IDF, Google Maps, Travel Reviews

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.

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Author Biography

Aulia Dian Agustina, Universitas Amikom Purwokerto

Student of Informatic Departement, Faculty of Computer Science, Universitas Amikom Purwokerto

References

H. Fang, G. Xu, Y. Long, and W. Tang, “An effective ELECTRA-based pipeline for sentiment analysis of tourist attraction reviews,” Appl. Sci., vol. 12, no. 21, p. 10881, 2022.

F. Borrajo-Millán, M.-M. Alonso-Almeida, M. Escat-Cortes, and L. Yi, “Sentiment analysis to measure quality and build sustainability in tourism destinations,” Sustainability, vol. 13, no. 11, p. 6015, 2021.

Y. Qin, X. Wang, and Z. Xu, “Ranking tourist attractions through online reviews: A novel method with intuitionistic and hesitant fuzzy information based on sentiment analysis,” Int. J. fuzzy Syst., vol. 24, no. 2, pp. 755–777, 2022.

I. P. Windasari and D. Eridani, “Sentiment analysis on travel destination in Indonesia,” in 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 2017, pp. 276–279.

Y. Luo, X. Zhang, Y. Qin, Z. Yang, and Y. Liang, “Tourism Attraction Selection with Sentiment Analysis of Online Reviews Based on Probabilistic Linguistic Term Sets and the IDOCRIW-COCOSO Model: Y. Luo et al.,” Int. J. Fuzzy Syst., vol. 23, no. 1, pp. 295–308, 2021.

M. A. Wirahudha, D. Damayanti, and D. A. Megawaty, “Sentiment Analysis of Comments on Tourist Attractions In Lampung Province Using The Naive Bayes Method,” J. Tek. Inform., vol. 6, no. 2, pp. 935–950, 2025.

S. P. Saharani and W. Yustanti, “Sentiment Analysis and Topic Modeling of Tourist Attractions in Gresik Regency Using the BERT Method,” J. Emerg. Inf. Syst. Bus. Intell., vol. 7, no. 1, pp. 62–70, 2026.

N. W. S. Saraswati, I. K. G. D. Putra, M. Sudarma, and I. M. Sukarsa, “The Image of Tourist Attraction in Bali Based on Big Data Analytics and Sentiment Analysis,” in 2023 International Conference on Smart-Green Technology in Electrical and Information Systems (ICSGTEIS), 2023, pp. 82–87.

D. I. Af’idah, R. Kusumaningrum, and B. Surarso, “Long short term memory convolutional neural network for Indonesian sentiment analysis towards touristic destination reviews,” in 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), 2020, pp. 630–637.

A. Larasati, J. Sayono, A. Purnomo, E. Mohamad, M. Farhan, and P. Rahmawati, “Applying web mining and sentiment analysis to assess tourists review on Batu City tourist destination,” in 2020 4th International Conference on Vocational Education and Training (ICOVET), 2020, pp. 63–68.

E. P. Harahap, H. D. Purnomo, A. Iriani, I. Sembiring, and T. Nurtino, “Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the k-nearest neighbor method,” Comput. Sci. Inf. Technol., vol. 5, no. 1, pp. 19–28, 2024.

A. R. Setyawan, L. H. Suadaa, and B. Yuniarto, “Aspect-based sentiment analysis using adaptive aspect on tourist reviews in Jakarta,” Sist. J. Sist. Inf., vol. 13, no. 6, pp. 2456–2466, 2024.

R. A. N. Nayoan, A. F. Hidayatullah, and D. H. Fudholi, “Convolutional neural networks for Indonesian aspect-based sentiment analysis tourism review,” in 2021 9th International Conference on Information and Communication Technology (ICoICT), 2021, pp. 60–65.

E. D. Madyatmadja, J. Suhartono, C. Walandi, D. P. Hartanujaya, M. Budiharjo, and V. Febrian, “Harmonizing Sentiments: User Reviews from Social Media of Kuta Beach as a Tourist Attraction,” in 2024 Beyond Technology Summit on Informatics International Conference (BTS-I2C), 2024, pp. 462–467.

S. Kurniawan, A. S. Pramayoga, and Y. F. Ashari, “Sentiment Analysis in Coastal Tourism: A Systematic Literature Review,” Eng. Technol. & Appl. Sci. Res., vol. 15, no. 6, pp. 30226–30233, 2025.

I. Santiko, T. R. Soeprobowati, B. Surarso, and I. Tahyudin, “Traditional-Enhance-Mobile-Ubiquitous-Smart: Model Innovation in Higher Education Learning Style Classification Using Multidimensional and Machine Learning Methods,” J. Appl. Data Sci., vol. 6, no. 1, pp. 753–772, 2025, doi: https://doi.org/10.47738/jads.v6i1.598.

A. Santosa, M. I. S. Kusnadi, A. H. H. Rumainum, and A. A. S. Gunawan, “Exploring Tourist Movement in Central Java Using Network Science and Sentiment Analysis,” in 2025 4th International Conference on Electronics Representation and Algorithm (ICERA), 2025, pp. 310–315.

D. D. Purwanto, “Empirical Evaluation of IndoBERT and LSTM for Sentiment Analysis of Tourism Reviews: A Data-Driven Study on Kenjeran Park,” J. Tek. Inform., vol. 7, no. 1, pp. 463–474, 2026.

R. I. Perwira, V. A. Permadi, D. I. Purnamasari, R. P. Agusdin, and others, “Domain-specific fine-tuning of IndoBERT for aspect-based sentiment analysis in Indonesian travel user-generated content,” J. Inf. Syst. Eng. Bus. Intell., vol. 11, no. 1, pp. 30–40, 2025.

J. Arnandy, I. Budi, A. Ramadiah, P. K. Putra, and A. B. Santoso, “Aspect-Based Sentiment Analysis In The Asean Tourism Sector: A Systematic Literature Review,” J. Impresi Indones., vol. 4, no. 11, pp. 5316–5328, 2025.