Analisis Sentimen Berbasis Aspek dan Pemodelan Topik pada Candi Borobudur dan Candi Prambanan

##plugins.themes.academic_pro.article.main##

Dian Arianto
Indra Budi

Abstract

This study focuses on conducting aspect-based sentiment analysis and topic modelling of tourism destinations in Indonesia, which are Borobudur Temple and Prambanan Temple using Google Maps and Tripadvisor user reviews. Aspect-based sentiment analysis was done using five classical machine learning algorithms, which are NaïveBayes (NB), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Extra Trees (ET) using the unigram+bigram+trigram feature and the application of combination of the use of training and test data, stopwords removal, stemming, emoji processing, and over-sampled training data. The performance of models was evaluated by comparing F1-scores on each experimental result. Topic modelling was carried out using Latent Dirichlet Allocation (LDA) method which evaluated by its coherence score. The results show that LR is a model that can predict data well in almost all scenarios in this study with the highest score on the Attraction aspect with a score of 84.4%, Amenity 84.2%, Accessibility 89.1%, Image 70%, and HR 92.8%. Meanwhile, DT can predict data well on the Price aspect with a score of 91.3%. From the results of topic modelling, we recommend some approaches for the development of tourism in Borobudur Temple and Prambanan Temple, one of which is the government can lower the price of admission to Prambanan Temple and Borobudur Temple for foreign tourists so that they can compete with tourist attractions in neighboring Indonesia because many reviews state that the price of entrance tickets to Prambanan Temple and Borobudur Temple is too expensive for foreigners.

##plugins.themes.academic_pro.article.details##

How to Cite
Arianto, D., & Budi, I. (2023). Analisis Sentimen Berbasis Aspek dan Pemodelan Topik pada Candi Borobudur dan Candi Prambanan. MULTINETICS , 8(2), 141–150. https://doi.org/10.32722/multinetics.v8i2.5056

References

  1. H. Widowati, “5 Tahun Terakhir, Rerata Pertumbuhan Kunjungan Wisatawan Mancanegara 14%,” Https://Databoks.Katadata.Co.Id, 2019. https://databoks.katadata.co.id/datapublish/2019/07/17/5-tahun-terakhir-rerata-pertumbuhan-kunjungan-wisawatan-mancanegara-14 (diakses Feb 03, 2020).
  2. Bisnis.com, “Jumlah Kunjungan Wisatawan pada 2019 Diprediksi 16 Juta Orang.” https://ekonomi.bisnis.com/read/20200102/12/1186286/jumlah-kunjungan-wisatawan-pada-2019-diprediksi-16-juta-orang (diakses Jul 16, 2020).
  3. Andrea Lidwina, “Jumlah Kunjungan Turis Asing 2019 Kembali Meleset dari Target.” https://databoks.katadata.co.id/datapublish/2020/02/03/jumlah-kunjungan-turis-asing-2019-kembali-meleset-dari-target (diakses Jul 01, 2022).
  4. R. Murphy, “Comparison of Local Review Sites: Which Platform is Growing the Fastest?” https://www.brightlocal.com/research/comparison-of-local-review-sites/ (diakses Jun 27, 2020).
  5. World Tourism Organization, A Practical Guide to Tourism Destination Management. 2007. doi: 10.18111/9789284412433.
  6. B. Liu, “Sentiment analysis: Mining opinions, sentiments, and emotions,” Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, no. May, hlm. 1–367, 2012, doi: 10.1017/CBO9781139084789.
  7. M. R. Brett, “Topic Modeling: A Basic Introduction Journal of Digital Humanities,” Journal of Digital Humanities, 2012. http://journalofdigitalhumanities.org/2-1/topic-modeling-a-basic-introduction-by-megan-r-brett/ (diakses Jul 15, 2020).
  8. P. Prameswari, Zulkarnain, I. Surjandari, dan E. Laoh, “Mining online reviews in Indonesia’s priority tourist destinations using sentiment analysis and text summarization approach,” Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017, no. iCAST, hlm. 121–126, 2017, doi: 10.1109/ICAwST.2017.8256429.
  9. Munawir, M. D. Koerniawan, dan B. J. Dewancker, “Visitor perceptions and effectiveness of place branding strategies in thematic parks in Bandung City using text mining based on google maps user reviews,” Sustainability (Switzerland), vol. 11, no. 7, 2019, doi: 10.3390/SU11072123.
  10. R. A. M. Herry Irawan, Gina Akmalia, “Mining Tourist ’ s Perception toward Indonesia Tourism Destination Using Sentiment Analysis and Topic Modelling,” no. 1, 2019.
  11. I. L. Laily, I. Budi, A. B. Santoso, dan P. K. Putra, “Mining Indonesia Tourism’s Reviews to Evaluate the Services Through Multilabel Classification and LDA,” 2020.
  12. M. O. Ibrohim dan I. Budi, “Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter,” Proceedings ofthe Third Workshop on Abusive Language Online, hlm. 46–57, 2019, doi: 10.18653/v1/w19-3506.
  13. P. K. Novak, J. Smailović, B. Sluban, dan I. Mozetič, “Sentiment of emojis,” PLoS One, vol. 10, no. 12, hlm. 1–22, 2015, doi: 10.1371/journal.pone.0144296.
  14. A. Suciati dan I. Budi, “Aspect-based Opinion Mining for Code-Mixed Restaurant Reviews in Indonesia,” Proceedings of the 2019 International Conference on Asian Language Processing, IALP 2019, hlm. 59–64, 2019, doi: 10.1109/IALP48816.2019.9037689.
  15. D. Arianto dan I. Budi, “Aspect-based Sentiment Analysis on Indonesia’s Tourism Destinations Based on Google Maps User Code-Mixed Reviews (Study Case: Borobudur and Prambanan Temples),” dalam Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation, 2020, hlm. 359–367.
  16. F. Pedregosa, R. Weiss, dan M. Brucher, “Scikit-learn : Machine Learning in Python,” vol. 12, hlm. 2825–2830, 2011.
  17. A. Tripathy, A. Agrawal, dan S. K. Rath, “Classification of sentiment reviews using n-gram machine learning approach,” Expert Syst Appl, vol. 57, no. March 2016, hlm. 117–126, 2016, doi: 10.1016/j.eswa.2016.03.028.
  18. A. Wibowo, “10 Fold-Cross Validation – MTI,” 2017. https://mti.binus.ac.id/2017/11/24/10-fold-cross-validation/ (diakses Jul 16, 2020).
  19. P. Huilgol, “Accuracy vs . F1-Score,” 2019. https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237beca2
  20. C. Sievert, K. E. Shirley, dan N. York, “LDAvis : A method for visualizing and interpreting topics,” Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, hlm. 63–70, 2014.
  21. E. Rendón, R. Alejo, C. Castorena, F. J. Isidro-ortega, dan E. E. Granda-gutiérrez, “Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem,” Applied Sciences, 2020, doi: 10.3390/app10041276.