DOI: https://doi.org/10.21456/vol14iss4pp403-407

Analisis Sentimen Ulasan Wisatawan Terhadap Alun-Alun Kota Batam: Perbandingan Kinerja Metode Naive Bayes dan Support Vector Machine

*John Friadi scopus  -  Universitas Batam, Indonesia
Dwi Ely Kurniawan orcid scopus  -  Politeknik Negeri Batam, Indonesia
Open Access Copyright (c) 2024 Jurnal Sistem Informasi Bisnis

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Abstract

Batam City, as a rapidly developing tourism destination in Indonesia, continues to strive to enhance the potential of its tourist attractions to attract more visitors. The assessment of reviews from tourists is crucial in identifying necessary development measures to improve the quality of tourist attractions. This research aims to analyze the sentiment of reviews for the Alun-Alun Kota Batam tourist destination by leveraging data from Google Maps. Two classification methods, Naive Bayes and Support Vector Machine, are employed for sentiment analysis, and their performances are compared. From 1140 collected reviews, the data is categorized into three labels: positive, negative, and neutral. The research results indicate that the Support Vector Machine method achieves higher accuracy (94%) compared to Naive Bayes (83%). This study contributes insights into visitor sentiments towards Alun-Alun Kota Batam, with implications for policy development and more effective actions in enhancing local tourism appeal.

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Keywords: Sentiment Analysis; Tourist Reviews; Alun-Alun Kota Batam; Naive Bayes; Support Vector Machine.

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  4. Han, J., Pei, J., Kamber, M., 2011. Data mining: concepts and techniques. Elsevier
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  6. Ririanti, N.P., Purwinarko, A., 2021. Implementation of Support Vector Machine Algorithm with Correlation-Based Feature Selection and Term Frequency Inverse Document Frequency for Sentiment Analysis Review Hotel. Scientifics Journal of Informatics, 8(2), 297-303. https://doi.org/10.15294/sji.v8i2.29992
  7. Rifa'i, A., Sujaini, H., Prawira, D., 2021. Sentiment Analysis Objek Wisata Kalimantan Barat pada Google Maps Menggunakan Metode Naive Bayes. Jurnal Edukasi dan Penelitian Informatika, 7(3), 400-407. https://dx.doi.org/10.26418/jp.v7i3.48132
  8. Raharjo, R.A., Sunarya, I.M.G., Divayana, D.G.H., 2022. Perbandingan Metode Naive bayes Classifier dan Support Vector Machine Pada Kasus Analisis Sentimen Terhadap Data Vaksin Covid-19 di Twitter. Jurnal Ilmiah Elektronika dan Komputer, 15(2) 456-464
  9. Solanki, S.D., Solanki, A.D., Borah, S., 2021. Assimilate Machine Learning Algorithms in Big Data Analytics: Review, in Applications of Machine Learning in Big-Data Analytics and Cloud Computing. River Publishers, 81-114
  10. Somantri, O., Maharrani, R.H., Purwaningrum, S., 2023. Coastal Sentiment Review Using Naive bayes with Feature Selection Genetic Algorithm. Scientific Journal of Informatics, 10(3), 127-136. https://doi.org/10.15294/sji.v10i3.43988
  11. Sun, S., Luo, C., Chen, J., 2017. A Review of Natural Language Processing Techniques for Opinion Mining Systems. Information Fusion, 36, 10-25. https://doi.org/10.1016/j.inffus.2016.10.004
  12. Surahman, A., Octaviansyah, A.F., Darwis, D., 2020. Ekstraksi Data Produk E-Marketplace Sebagai Strategi Pengolahan Segmentasi Pasar Menggunakan Web Crawler. SISTEMASI: Jurnal Sistem Informasi, 9(1), 73-81. https://doi.org/10.32520/stmsi.v9i1.580
  13. Thaha, A.R., Aziz, F., 2020. Text Mining on Tourism Destinations in Bandung Raya (Case Study: Tangkuban Perahu and Kawah Putih). Jurnal Sekretaris dan Administrasi Bisnis, 4(2), 146-156. https://doi.org/10.31104/jsab.v4i2.172
  14. Wardani, F.K., 2019. Analisis Sentimen untuk Pemeringkatan Popularitas Situs Belanja Online di Indonesia Menggunakan Metode Naive bayes (Studi Kasus Data Sekunder). Surabaya: Institut Bisnis dan Informatika STIKOM
  15. Marwanta, Y.Y., Badiyanto, 2023. Analisis Sentimen Pencitraan Perguruan Tinggi di Yogyakarta Menggunakan Metode Naive Bayes Classifier. Journal of Applied Informatic and Computing (JAIC), 7(1), 21-27
  16. Zhang, F., Fleyeh, H., Wang, X., Lu, M., 2019. Construction Site Accident Analysis using Text Mining and Natural Language Processing Techniques. Automation in Construction, 99, 238-248. https://doi.org/10.1016/j.autcon.2018.12.016
  17. Zulfikar, W.B., Atmadja, A.R., Pratama, S.F., 2023. Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes. Scientific Journal of Informatics. Scientific Journal of Informatics, 10(1), 25-34. https://doi.org/10.15294/sji.v10i1.39952

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