ANALISIS SENTIMEN WISATA AIR TERJUN DI KABUPATEN LOMBOK TENGAH MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

Authors

  • M. Syamsul Hadi STMIK Lombok
  • Jihadul Akbar STMIK Lombok
  • Muhammad Fauzi Zulkarnain STMIK Lombok

DOI:

https://doi.org/10.36080/skanika.v8i2.3578

Keywords:

Central Lombok, Sentiment Analysis, SVM, TF-IDF, Waterfall Tourism

Abstract

The tourism potential of waterfalls in Central Lombok Regency is quite prominent; however, it has not been fully optimized. With the advancement of digital technology, user reviews on online platforms can serve as valid indicators to assess the quality of a tourist destination. This study aims to analyze the sentiment of visitor reviews regarding waterfall tourist attractions using the Support Vector Machine (SVM) algorithm. Data were collected through web scraping from Google Maps, then processed through several preprocessing stages, automatically labeled using IndoBERT, and features were extracted using the TF-IDF method. A total of 1,250 reviews were analyzed and classified into three sentiment categories: positive, neutral, and negative. Three types of SVM algorithms were tested: LinearSVC, RBF kernel, and Polynomial kernel. Based on the results, LinearSVC achieved the best performance with an accuracy of 84% and an F1-score of 86%. These findings indicate that a machine learning-based approach, particularly SVM, is highly effective in automatically and systematically identifying visitor perceptions. The resulting data may also serve as a reference in developing tourism policies grounded in empirical evidence.

 

Downloads

Download data is not yet available.

References

[1] A. Hanan, P. Gadu, and A. Sriwi, “Strategi Pengebangan Daya Tari Wisata Air Terjun Babak Pelangi Desa Lantan Kabupaten Lombok Tengah,” J. Responsible Tour., vol. 3, no. 1, pp. 65–76, 2023, doi: 10.47492/jrt.v3i1.2720.

[2] BPS NTB, “Perkembangan PariwisataNusa Tenggara Barat,” no. 51, 2024, [Online]. Available: https://ntb.bps.go.id/id/pressrelease/2024/05/02/988/ntb--maret-2024--perkembangan-pariwisata.html

[3] C. P. Helena Panjaitan and C. Supriadi, “Analisis Sentimen Pengunjung Wisata Heritage Kota Semarang Menggunakan Naive Bayes Pada Ulasan Google Maps,” J. Elektron. dan Teknol. Inf., vol. 4, no. 2, pp. 2721–9380, 2023, doi: https://doi.org/10.5201/jet.v4i2.412.

[4] O. I. Gifari, M. Adha, F. Freddy, and F. F. S. Durrand, “Film Review Sentiment Analysis Using TF-IDF and Support Vector Machine,” JIfoTech: Journal Information Technology, vol. 2, no. 1, pp. 36–40, 2022, doi: https://doi.org/10.46229/jifotech.v2i1.330.

[5] Alfandi Safira and F. N. Hasan, “Analisis Sentimen Masyarakat Terhadap Paylater Menggunakan Metode Naive Bayes Classifier,” Zo. J. Sist. Inf., vol. 5, no. 1, pp. 59–70, 2023, doi: 10.31849/zn.v5i1.12856.

[6] M. S. Syahlan, D. Irmayanti, and S. Alam, “Analisis Sentimen Terhadap Tempat Wisata Dari Komentar Pengunjung Dengan Menggunakan Metode Support Vector Machine (Svm),” Simtek J. Sist. Inf. dan Tek. Komput., vol. 8, no. 2, pp. 315–319, 2023, doi: 10.51876/simtek.v8i2.281.

[7] N. Ikhsan, S. P. Damayanti, and P. Gadu, “Konsep Daya Tarik Wisata Berbasis Fasilitas Dalam Meningkatkan Minat Kunjungan Wisatawan Ke Wisata Benang Kelambu Di Desa Aik Berik,” vol. 4, no. 2, pp. 1–23, 2016, doi: https://doi.org/10.47492/jrt.v4i2.3664.

[8] I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023, doi: 10.37905/jjeee.v5i1.16830.

[9] J. E. Br Sinulingga and H. C. K. Sitorus, “Analisis Sentimen Opini Masyarakat terhadap Film Horor Indonesia Menggunakan Metode SVM dan TF-IDF,” J. Manaj. Inform., vol. 14, no. 1, pp. 42–53, 2024, doi: 10.34010/jamika.v14i1.11946.

[10] A. Afif, “Analisis Metode Klasifikasi Data Naïve Bayes dan SVM Dalam Menentukan Keunikan Hotel,” JUTEKINF (Jurnal Teknol. Komput. dan Informasi), vol. 11, no. 1, pp. 38–46, 2023, doi: 10.52072/jutekinf.v11i1.526.

[11] A. Baita, Y. Pristyanto, and N. Cahyono, “Analisis Sentimen Mengenai Vaksin Sinovac Menggunakan Algoritma Support Vector Machine (SVM) Dan K-Nearest Neighbor (KNN),” Infos, vol. 4, no. 2, pp. 42–42, 2021, doi: https://doi.org/10.24076/infosjournal.2021v4i2.687.

[12] H. Sibyan and N. Hasanah, “Analisis Sentimen Ulasan Pada Wisata Dieng Dengan Algoritma K-Nearest Neighbor (K-NN),” J. Penelit. dan Pengabdi. Kpd. Masy. UNSIQ, vol. 9, no. 1, pp. 38–47, 2022, doi: 10.32699/ppkm.v9i1.2218.

[13] N. Surhasanah, R. Dwiyansaputra, S. I. Murpratiwi, and A. Aranta, “Analisis Sentimen Pengguna Platform Media Sosial X Pada Topik Pemilihan Presiden 2024 Menggunakan Perbandingan Model,” vol. 9, no. 1, pp. 626–634, 2025, doi: https://doi.org/10.36040/jati.v9i1.12430.

[14] T. Tarwoto, R. Nugroho, N. Azka, and W. S. R. Graha, “Analisis Sentimen Ulasan Aplikasi Mobile JKN di Google,” Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 9, no. pp. 495–505, 2025, doi: https://doi.org/10.35870/jtik.v9i2.3340.

[15] K. Tri Putra, M. A. Hariyadi, and C. Crysdian, “Perbandingan Feature Extraction TF-IDF dan Bow untuk Analisis Sentimen Berbasis SVM,” Jurnal Mandalika, vol. 3, no. 2, pp. 1449–1463, 2023, doi: https://doi.org/10.36312/jcm.v3i2.

[16] F. Rifaldy, Y. Sibaroni, and S. S. Prasetiyowati, “Effectiveness of Word2vec and TF-IDF in Sentiment Classification on Online Investment Platforms Using Support Vector Machine,” vol. 10, no. 2, pp. 863–874, 2025, doi: https://doi.org/10.29100/jipi.v10i2.6055.

[17] J. Ipmawati, S. Saifulloh, and K. Kusnawi, “Analisis Sentimen Tempat Wisata Berdasarkan Ulasan pada Google Maps Menggunakan Algoritma Support Vector Machine,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 1, pp. 247–256, 2024, doi: 10.57152/malcom.v4i1.1066.

[18] S. Rabbani, D. Safitri, and N. Rahmadhani, “Comparative Evaluation of SVM Kernels for Sentiment Classification in Fuel Price Increase Analysis Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM,” vol. 3, no. 2, pp. 153–160, 2023, doi: https://doi.org/10.57152/malcom.v3i2.897.

[19] F. N. Salsabilla, and A. Witanti., “Analisis asentimen Akhir Masa Jabatan Presiden Jokowi Pada Media Sosial X Menggunakan Naïve Bayes,” SKANIKA: Sistem Komputer dan Teknik Informatika, vol. 8, no. 1, pp. 106–115, 2025, doi: https://doi.org/10.36080/skanika.v8i1.3331.

Downloads

Published

2025-07-31

How to Cite

[1]
M. Syamsul Hadi, Jihadul Akbar, and Muhammad Fauzi Zulkarnain, “ANALISIS SENTIMEN WISATA AIR TERJUN DI KABUPATEN LOMBOK TENGAH MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)”, SKANIKA, vol. 8, no. 2, pp. 318–329, Jul. 2025.

Issue

Vol. 8 No. 2 (2025): Jurnal SKANIKA Juli 2025

Section

Articles

License

Copyright (c) 2025 M. Syamsul Hadi, Jihadul Akbar, Muhammad Fauzi Zulkarnain

Creative Commons License

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

CC BY-SA 4.0

Creative Commons Attribution-ShareAlike 4.0 International

This license requires that reusers give credit to the creator. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes. If others remix, adapt, or build upon the material, they must license the modified material under identical terms.

BY: Credit must be given to you, the creator.

SA: Adaptations must be shared under the same terms.ng