Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation

  • Ni Luh Putu Merawati Putu Universitas Bumigora
  • Ahmad Zuli Amrullah Universitas Bumigora
  • Ismarmiaty Universitas Bumigora
Keywords: sentiment analysis, naive bayes,topic modelling, LDA, lombok tourism

Abstract

Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism.

 

Downloads

Download data is not yet available.

References

N. Islamy, “Analisis Sektor Potensial, Dapatkah Pariwisata Menjadi Lokomotif Baru Ekonomi Nusa Tenggara Barat?,” J. Indones. Tour. Hosp. Recreat., vol. 2, no. 1, pp. 1–10, 2019.

K. a n o m K a n o m, “Strategi Pengembangan Kuta Lombok Sebagai Destinasi Pariwisata Berkelanjutan,” J. Master Pariwisata, vol. 1, pp. 25–42, 2015.

H. S. Utama, D. Rosiyadi, B. S. Prakoso, and D. Ariadarma, “Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 243–250, 2019.

S. N. J. Fitriyyah, N. Safriadi, and E. E. Pratama, “Analisis Sentimen Calon Presiden Indonesia 2019 dari Media Sosial Twitter Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 5, no. 3, pp. 279–285, 2019.

R. Ferdiana, F. Jatmiko, D. D. Purwanti, A. S. T. Ayu, and W. F. Dicka, “Dataset Indonesia untuk Analisis Sentimen,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 4, pp. 334–339, 2019.

A. Alamsyah, W. Rizkika, D. D. A. Nugroho, F. Renaldi, and S. Saadah, “Dynamic large scale data on Twitter using sentiment analysis and topic modeling case study: Uber,” in 2018 6th International Conference on Information and Communication Technology, ICoICT 2018, 2018, vol. 0, no. c, pp. 254–258.

R. Ardianto, T. Rivanie, Y. Alkhalifi, F. S. Nugraha, and W. Gata, “Sentiment Analysis on E-Sports For Education Curriculum Using Naive Bayes and Support Vector Machine,” J. Comput. Sci. Inf., vol. 13, no. 2, pp. 109–122, 2020.

M. Cendana and S. D. H. Permana, “Pra-Pemrosesan Teks Pada Grup Whatsapp Untuk Pemodelan Topik,” Junal Mantik Penusa, vol. 3, no. 3, pp. 107–116, 2019.

O. Somantri and D. Dairoh, “Analisis Sentimen Penilaian Tempat Tujuan Wisata Kota Tegal Berbasis Text Mining,” J. Edukasi dan Penelit. Inform., vol. 5, no. 2, pp. 191–196, 2019.

S. Fanissa, M. A. Fauzi, and S. Adinugroho, “Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naive Bayes dan Seleksi Fitur Query Expansion Ranking,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 8, pp. 2766–2770, 2018.

Murnawan and A. Sinaga, “Pemanfaatan Analisis Sentimen untuk Pemeringkatan Popularitas Tujuan Wisata,” J. Penelit. Pos dan Inform., vol. 7, no. 2, pp. 109–120, 2017.

M. A. Ulfa, B. Irmawati, and A. Y. Husodo, “Twitter Sentiment Analysis using Naive Bayes Classifier with Mutual Information Feature Selection,” J. Comput. Sci. Informatics Eng., vol. 2, no. 2, pp. 106–111, 2018.

G. R. Gustisa Wisnu, Ahmadi, A. R. Muttaqi, A. B. Santoso, P. K. Putra, and I. Budi, “Sentiment analysis and topic modelling of 2018 central java gubernatorial election using twitter data,” 2020 Int. Work. Big Data Inf. Secur. IWBIS 2020, pp. 35–40, 2020.

P. M. R. C. Dinatha and N. A. Rakhmawati, “Komparasi Term Weighting dan Word Embedding pada Klasifikasi Tweet Pemerintah Daerah,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 2, pp. 155–161, 2020.

P. M. Prihatini, “Implementasi Ekstraksi Fitur Pada Pengolahan Dokumen Berbahasa Indonesia,” J. Matrix, vol. 6, no. 3, pp. 174–178, 2016.

V. Balakrishnan and W. Kaur, “String Based Multinomial Naive Bayes for Emotion Detection among Facebook Diabetes Community,” Procedia Comput. Sci., vol. 159, pp. 30–37, 2019.

D. Blei, L. Carin, and D. Dunson, “Probabilistic topic models,” IEEE Signal Process. Mag., vol. 27, no. 6, pp. 55–65, 2010.

D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.

W. Chen, Z. Xu, X. Zheng, Q. Yu, and Y. Luo, “Research on sentiment classification of online travel review text,” Appl. Sci., vol. 10, no. 15, 2020.

K. Stevens, P. Kegelmeyer, D. Andrzejewski, and D. Buttler, “Exploring topic coherence over many models and many topics,” in EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference, 2012, no. July, pp. 952–961.

Published
2021-02-20
How to Cite
Putu, N. L. P. M., Ahmad Zuli Amrullah, & Ismarmiaty. (2021). Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 123 - 131. https://doi.org/10.29207/resti.v5i1.2587
Section
Artikel Teknologi Informasi