Indonesian Journal of Cultural and Community Development

Vol. 16 No. 3 (2025): September
Tourism and Hospitality Development Articles

YouTube Sentiment Reveals Public Perception of Lapindo Mud Tourism: Analisis Sentimen YouTube Mengungkap Persepsi Publik terhadap Pariwisata Lumpur Lapindo

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  • Muhammad Iqbal Nahariqi,
  • Yulian Findawati,
  • Irwan Alnanrus Kautsar,
  • Mochamad Alfan Rosid
Muhammad Iqbal Nahariqi
Program Studi Teknik Informatika, Universitas Muhammadiyah Sidoarjo
Yulian Findawati
Program Studi Teknik Informatika, Universitas Muhammadiyah Sidoarjo
Irwan Alnanrus Kautsar
Program Studi Teknik Informatika, Universitas Muhammadiyah Sidoarjo
Mochamad Alfan Rosid
Program Studi Teknik Informatika, Universitas Muhammadiyah Sidoarjo
Categories

Published 2025-09-12

Keywords

  • Sentiment Analysis,
  • YouTube Comments,
  • Lapindo Mud Tourism,
  • K-Nearest Neighbor,
  • TF-IDF

Copyright (c) 2025 Muhammad Iqbal Nahariqi, Yulian Findawati, Irwan Alnanrus Kautsar, Mochamad Alfan Rosid

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

YouTube Sentiment Reveals Public Perception of Lapindo Mud Tourism: Analisis Sentimen YouTube Mengungkap Persepsi Publik terhadap Pariwisata Lumpur Lapindo. (2025). Indonesian Journal of Cultural and Community Development, 16(3). https://doi.org/10.21070/ycffb102

Abstract

General Background: Social media platforms provide extensive public opinion data that can be utilized to understand perceptions of tourism destinations. Specific Background: YouTube comments related to Lapindo Mud tourism contain diverse viewpoints reflecting visitors’ experiences and societal responses to the site. Knowledge Gap: Limited studies analyze public sentiment toward disaster-related tourism destinations using machine learning–based text mining approaches. Aims: This study classifies YouTube user comments to identify sentiment patterns regarding Lapindo Mud tourism using TF-IDF weighting and the K-Nearest Neighbor (K-NN) algorithm. Results: From 520 labeled comments, the model achieved 78% accuracy, with higher precision and recall in identifying negative sentiment than positive sentiment. Novelty: The study integrates sentiment analysis, expert-based labeling, and tourism perception assessment to examine how digital discourse represents a disaster-turned-tourism site. Implications: Findings provide insights for tourism stakeholders and local authorities to understand public perception and inform strategies for managing the image and communication of Lapindo Mud as a tourism destination.

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References

  1. [1] A. N. Putra, “Sentiment Analysis of YouTube Users Toward the 2022 New Currency Emission Using Naïve Bayes Classifier,” Jurnal Teknologi dan Sistem Komputer, vol. 5, no. 1, pp. 45–52, Jan. 2024.
  2. [2] A. Hendrawan and E. I. Sela, “Sentiment Analysis of YouTube Comments on the 2023 Global Recession Using LSTM,” Jurnal Indonesia Manajemen Informatika dan Komunikasi, vol. 5, no. 1, pp. 587–593, Jan. 2024.
  3. [3] H. Marlina, E. Elmayati, A. Zulius, and H. O. L. Wijaya, “Application of Random Forest Algorithm for Student Major Classification at SMA Negeri Tugumulyo,” Brahmana: Jurnal Penerapan Kecerdasan Buatan, vol. 4, no. 2, pp. 138–143, Jun. 2023.
  4. [4] M. H. Mahendra, D. T. Murdiansyah, and K. M. Lhaksmana, “Sentiment Analysis of COVID-19 Tweets Using K-Nearest Neighbor with TF-IDF and CountVectorizer Feature Extraction,” DIKE: Jurnal Ilmu Multidisiplin, vol. 1, no. 2, pp. 37–43, Aug. 2023.
  5. [5] M. Djufri, “Application of Web Scraping Techniques for Tax Potential Extraction (Case Study on Tokopedia, Shopee, and Bukalapak),” Jurnal BPPK: Badan Pendidikan dan Pelatihan Keuangan, vol. 13, no. 2, pp. 65–75, Dec. 2020.
  6. [6] A. E. Budiman and A. Widjaja, “Analysis of Text Preprocessing Influence on Plagiarism Detection in Undergraduate Thesis Documents,” Jurnal Teknik Informatika dan Sistem Informasi (JuTISI), vol. 6, no. 3, pp. 1–8, Dec. 2020.
  7. [7] M. Priandi and Painem, “Public Sentiment Analysis Toward Online Learning During the COVID-19 Pandemic on Twitter Using CountVectorizer and K-Nearest Neighbor,” in Proc. Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA), Jakarta, Indonesia, Sep. 15, 2021.
  8. [8] M. A. Rosid, “Improving Text Preprocessing for Student Complaint Document Classification Using Sastrawi,” in Proc. International Conference on Engineering Technology and Social Science (ICETsAS), IOP Conf. Series: Materials Science and Engineering, vol. 874, 2020, doi: 10.1088/1757-899X/874/1/012017.
  9. [9] R. Riyaddulloh and A. Romadhony, “Indonesian Text Normalization Based on Slang Dictionary: Case Study of Gadget Product Tweets on Twitter,” e-Proceeding of Engineering, vol. 8, no. 4, pp. 42173–42239, Aug. 2021.
  10. [10] F. Istighfarizky, N. A. S. ER, I. M. Widiartha, L. G. Astuti, I. G. N. A. C. Putra, and I. K. G. Suhartana, “Journal Classification Using KNN with Feature Selection Comparison,” Jurnal Elektronik Ilmu Komputer Udayana (JELIKU), vol. 11, no. 1, pp. 167–176, Aug. 2022.
  11. [11] A. N. H. Regita and I. Santoso, “Public Sentiment Analysis Toward Road Damage Takeover in Lampung Using K-Nearest Neighbor Algorithm,” IKRA-ITH Informatika: Jurnal Komputer dan Informatika, vol. 7, no. 2, pp. 176–182, Jul. 2023.
  12. [12] F. Amardita, R. A. Daeli, and I. Ginting, “Sentiment Analysis of Tourist Reviews at Paris Van Java Resort Bandung Using K-Nearest Neighbor and TF-IDF,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 1, pp. 62–68, Feb. 2022, doi: 10.30865/jurikom.v9i1.3793.
  13. [13] G. Pati and E. Umar, “Sentiment Analysis of Visitor Comments on Weekuri Lake Tourism Using Naïve Bayes and K-Nearest Neighbor,” Jurnal Media Informatika Budidarma, vol. 6, no. 4, pp. 2309–2315, Oct. 2022, doi: 10.30865/mib.v6i4.4635.
  14. [14] D. N. Larasakti, A. Aziz, and D. Aditya, “Sentiment Analysis of YouTube Video Comments Using K-Nearest Neighbor Method,” Jurnal Ilmiah Wahana Pendidikan, vol. 9, no. 5, pp. 132–142, Mar. 2023, doi: 10.5281/zenodo.7728573.
  15. [15] B. B. Baskoro, I. Susanto, and S. Khomsah, “Hotel Customer Sentiment Analysis in Purwokerto Using Random Forest and TF-IDF,” Jurnal INISTA, vol. 3, no. 2, pp. 21–29, May 2021, doi: 10.20895/inista.v3i2.