Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods
DOI:
https://doi.org/10.34148/teknika.v14i1.1198Keywords:
TripAdvisor Sentiment Analysis, Random Forest, Support Vector Machine, Naïve BayesAbstract
TripAdvisor faces problems in improving the quality of service on its application, namely the presence of unexpected or non-functional features, which can affect the user experience and reduce trust in the application. This research aims to develop an application capable of performing sentiment analysis on TripAdvisor application user reviews on the Google Play Store with negative, positive, and neutral classifications using the Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The RF method was chosen in this study because of its ability to handle large and complex data very accurately, while SVM is able to classify data on a large scale and is resistant to overfitting, while NB is able to classify text with clear probabilities. The Lexicon-based method as data labelling. The results of sentiment analysis from 1500 reviews with web scrapping show the classification of positive, negative, and neutral sentiments of 48, 726, and 646 data, respectively. Model performance in RF, SVM, and NB testing gets an accuracy value of 94%, 93.6%, and 77.8%, respectively. The RF model produces the best accuracy compared to other methods. The RF model produces the best accuracy compared to other methods. The results of sentiment analysis from 1500 user reviews allow developers to identify features that are often criticized or do not function properly in their application services.
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[1] P. U. Rukmana, O. N. Pratiwi, and H. Fakhrurroja, “Perbandingan Analisis Sentimen Aplikasi Traveloka dan Tiket.com pada Twitter dengan Metode Support Vector Machine,” J. Sist. Cerdas, vol. 6, no. 3, pp. 241–250, 2023, doi: 10.37396/jsc.v6i3.350.
[2] J. Nurvania, J. Jondri, and K. M. Lhaksamana, “Analisis Sentimen Pada Ulasan di TripAdvisor Menggunakan Metode Long Short-Term Memory (LSTM),” eProceedings Eng., vol. 8, no. 4, 2021, [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/15240
[3] A. M. Ndapamuri, D. Manongga, and A. Iriani, “Analisis Sentimen Ulasan Aplikasi Tripadvisor Dengan Metode Support Vector Machine, K-Nearest Neighbor, Dan Naive Bayes,” INOVTEK Polbeng - Seri Inform., vol. 8, no. 1, p. 127, 2023, doi: 10.35314/isi.v8i1.3260.
[4] M. Naufal, B. Balit, F. S. Utomo, I. S. Program, and C. Java, “Sentiment Analysis of pegipegi . com Review on Google Play Store with Naïve Bayes,” vol. 13, pp. 1044–1053, 2024, [Online]. Available: https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/3913
[5] T. S. Kasus, P. Pada, and S. Tripadvisor, “Analisis Sentimen Pelanggan Hotel di Purwokerto Menggunakan Metode Random Forest dan TF-IDF (Studi Kasus: Ulasan Pelanggan Pada Situs TRIPADVISOR),” vol. 8106, pp. 21–29, 2021, doi: 10.20895/INISTA.V3.
[6] Z. Jin, J. Shang, Q. Zhu, C. Ling, W. Xie, and B. Qiang, “RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12343 LNCS, pp. 503–515, 2020, doi: 10.1007/978-3-030-62008-0_35.
[7] A. Supian, B. T. Revaldo, N. Marhadi, - Rahmaddeni, and L. Efrizoni, “Penerapan SVM dan Word2Vec untuk Analisis Sentimen Ulasan Pengguna Aplikasi DANA ,” J. Ilm. Komputasi, vol. 23, no. 3 SE-Sistem Informasi, p. 4, Sep. 2024, doi: 10.32409/jikstik.23.3.3642.
[8] E. Damayanti, A. V. Vitianingsih, S. Kacung, H. Suhartoyo, and A. Lidya Maukar, “Sentiment Analysis of Alfagift Application User Reviews Using Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) Methods,” Decod. J. Pendidik. Teknol. Inf., vol. 4, no. 2, pp. 509–521, 2024, doi: 10.51454/decode.v4i2.478.
[9] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-Based Methods for Sentiment Analysis,” Comput. Linguist., vol. 37, pp. 267–307, Jun. 2011, doi: 10.1162/COLI_a_00049.
[10] G. R. Putri, M. A. Maulana, and S. Bahri, “Perbandingan Algoritma Naïve Bayes dan TextBlob Untuk Mendapatkan Analisis Sentimen Masyarakat Pada Sosial Media,” Teknika, vol. 13, no. 2, pp. 213–218, 2024, doi: 10.34148/teknika.v13i2.815.
[11] N. Wiliani, N. L. Chusna, and P. B. Ramadhan, Analisis Sentimen terhadap Pro Kontra Aksi Unjuk Rasa Mahasiswa dengan Naïve Bayes dan Information Gain. Penerbit NEM, 2023. [Online]. Available: https://books.google.co.id/books?id=RvTUEAAAQBAJ
[12] D. Jollyta, A. Hajjah, E. Haerani, and M. Siddik, Algoritma Klasifikasi untuk Pemula Solusi Python dan RapidMiner. Deepublish, 2023. [Online]. Available: https://books.google.co.id/books?id=jVjcEAAAQBAJ
[13] C. M. Putri, M. Afdal, R. Novita, and M. Mustakim, “Perbandingan Evaluasi Kernel Support Vector Machine dalam Analisis Sentimen Chatbot AI pada Ulasan Google Play Store,” J. Teknol. Sist. Inf. dan Apl., vol. 7, no. 3, pp. 1236–1245, 2024, doi: 10.32493/jtsi.v7i3.41354.
[14] A. H. Nurdy and A. Rahim, “Analisis Sentimen Ulasan Game Stumble Guys Pada Playstore Menggunakan Algoritma Naïve Bayes Sentiment Analysis of Stumble Guys Game Reviews on Playstore Using the Naïve Bayes Algorithm,” vol. 13, no. November, pp. 388–395, 2024, doi: 10.34148/teknika.v13i3.993.
[15] I. Tahyudin, Pengenalan Machine Learning Menggunakan Jupyter Notebook. in Mechine Learning. Zahira Media Publisher, 2020. [Online]. Available: https://books.google.co.id/books?id=_uMREAAAQBAJ
[16] W. S. Lestari and Y. M. Saragih, “Comparison of Deep Neural Networks and Random Forest Algorithms for Multiclass Stunting Prediction in Toddlers,” vol. 13, no. November, pp. 412–417, 2024, doi: 10.34148/teknika.v13i3.1063.
[17] D. A. Kristiyanti and Sri Hardani, “Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short-Term Memory (LSTM),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 3, pp. 722–732, 2023, doi: 10.29207/resti.v7i3.4737.
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