Exploring Customer Perceptions through Sentiment Analysis of Google Reviews at Rainbow Alamanda: SVM vs Naive Bayes Algorithm

Farizal Salman; Prima Dina Atika; Rafika Sari

Authors

Farizal Salman Informatika, Fakultas Ilmu Komputer, Universitas Bhayangkara Jakarta Raya
Prima Dina Atika Informatika, Fakultas Ilmu Komputer, Universitas Bhayangkara Jakarta Raya
Rafika Sari Informatika, Fakultas Ilmu Komputer, Universitas Bhayangkara Jakarta Raya

Keywords:

Sentiment analysis, Google Review, Rainbow Alamanda Park, Support Vector Machine, Naive Bayes

Abstract

As one of the popular family tourist destinations, Rainbow Alamanda Park has received thousands of reviews from visitors on the Google Review platform. These reviews reflect public perceptions of the quality of services and facilities offered, making it important to analyze them systematically. This study aims to analyze the sentiment of visitor reviews on Google Review regarding Rainbow Alamanda using two machine learning algorithms: Naive Bayes and Support Vector Machine (SVM), and to compare the performance of both methods. The research process follows the SEMMA approach (Sample, Explore, Modify, Model, Assess), utilizing a dataset of 2,394 reviews collected through web scraping techniques. The evaluation results show that the Naive Bayes method performed best with a training-to-testing data ratio of 70:30, achieving an accuracy of 86.32%, precision of 86.83%, recall of 85.81%, and an F1-score of 86.08%. Meanwhile, the SVM method with an RBF kernel (C=10, γ=0.1) achieved higher performance, with an accuracy of 88.44%, precision of 90.27%, recall of 88.31%, and an F1-score of 89.28%.

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References

Busulwa, R. (2024). Navigating Digital Transformation in Management. In Navigating Digital Transformation in Management. Taylor and Francis. https://doi.org/10.4324/9781003254614/NAVIGATING-DIGITAL-TRANSFORMATION-MANAGEMENT-RICHARD-BUSULWA/RIGHTS-AND-PERMISSIONS

Dewi, S. (2019). Komparasi Metode Algoritma Data Mining pada Prediksi Uji Kelayakan Credit Approval pada Calon Nasabah Kredit Perbankan. Jurnal Khatulistiwa Informatika, 7(1). https://doi.org/10.31294/JKI.V7I1.5744

Hamidah, D. A., Salkiawati, R., & Sari, R. (2024). Analisis Sentimen Ulasan Customer Kopi TMLST Menggunakan Algoritma Naïve Bayes. Journal of Students‘ Research in Computer Science, 5(1), 27–40. https://doi.org/10.31599/mrm89y71

Haq, F. U., & Rachmat, H. (2020). Penggunaan Google Review Sebagai Penilaian Kepuasan Pengunjung Dalam Pariwisata. Tornare - Journal of Sustainable Tourism Research, 2(1), 10–12. https://www.researchgate.net/profile/Hardik

Hrushikesha Mohanty, Prachet Bhuyan, & Deepak Chenthati. (2015). Big Data (H. Mohanty, P. Bhuyan, & D. Chenthati, Eds.; Vol. 11). Springer India. https://doi.org/10.1007/978-81-322-2494-5

Ipmawati, J., Saifulloh, S., & Kusnawi, K. (2024). Analisis Sentimen Tempat Wisata Berdasarkan Ulasan pada Google Maps Menggunakan Algoritma Support Vector Machine. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(1), 247–256. https://doi.org/10.57152/malcom.v4i1.1066

Pelangi, Kartika Chandra, & Rofiq Harun. (2024). Analisis Sentimen Objek Wisata Di Kabupaten Banggai Laut Menggunakan Metode Naive Bayes. Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer, 3(1), 68–74.

Sari, R., Helmy, N., & Alexander, A. D. (2024). Determining Sales Patterns Using the Apriori Algorithm: A Case Study of Unlocked Cafe’s Website Applications. PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic, 12(1), 189–200. https://doi.org/10.33558/piksel.v12i1.8908

Setyawan, Y. A., Nastiti, F. E., & Sari, A. A. (2025). Analisis Sentimen Ulasan Tempat Wisata Umbul Sigedang Pada Google Maps Menggunakan Algoritma Naive Bayes Dan Support Vector Machine Sentiment Analysis of Umbul Sigedang Tourist Attraction Reviews on Google Maps Using Naive Bayes Algorithm and Support Vector Machine. Jurnalnya Orang Pintar Komputer, 14(2). https://doi.org/10.30591/smartcomp.v13i1.7467

Syahlan, M. S., Irmayanti, D., & Alam, S. (2023). Analisis Sentimen Terhadap Tempat Wisata dari Komentar Pengunjung dengan Menggunakan Metode Support Vector Machine (SVM) (Studi Kasus: Taman Air Mancur Sri Baduga Purwakarta). Jurnal Sistem Informasi Dan Teknik Komputer, 8(2), 2502.

T. Jo. (2018). Text Mining: Concepts, Implementation, and Big Data Challenge. in Studies in Big Data. Springer International Publishing.

Tamara Cindy Samsita Rani, & Eka Sahputra. (2024). Penerapan Machine Learning terhadap Analisis Sentimen Masyarakat - Dalam Pemilihan Presiden Indonesia 2024. NEM.