Perbandingan Naïve Bayes dan Support Vector Machine Dalam Analisis Sentimen Google Maps Pusat Perbelanjaan
- Eliza Cahyaningrum Universitas Semarang, Semarang, Indonesia
-
Astrid Novita Putri
*
Universitas Semarang, Semarang,
Indonesia
(*) Corresponding Author
Abstract
The rapid growth of user reviews on Google Maps is not always accompanied by ease in understanding the sentiment contained within them, causing tourists and the general public to face difficulties in determining shopping centers with good reputation and service quality. The lack of information regarding visitor satisfaction levels, along with various facility-related issues such as crowd density, limited parking space, and the comfort of public facilities, combined with the large number of subjective and unstructured reviews, makes manual sentiment analysis ineffective and potentially leads to less accurate conclusions. This investigation aims to analyze sentiment from Google Maps reviews of shopping centers in the city of Semarang utilizing the Support Vector Machine (SVM) and Naïve Bayes methods. The data were collected from five shopping centers with the highest number of reviews in Semarang, namely Paragon Mall, Mall Ciputra, Java Mall, DP Mall, and Queen City Mall. The investigation method includes text preprocessing, TF-IDF weighting, and sentiment classification into three classes: negative, neutral, and positive. The dataset was divided into training and testing data with a ratio of 80:20. The outcomes reveal that the Naïve Bayes method achieved an accuracy of 85.56%, while the Support Vector Machine (SVM) method achieved an accuracy of 89.20%. Considering the outcomes, the SVM method performs better in classifying sentiment from Google Maps reviews of shopping centers in Semarang.
Downloads
References
R. T. Handayanto, Herlawati, P. D. Atika, F. N. Khasanah, A. Y. P. Yusuf, and D. Y. Saputra, “Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine,” J. Komtika (Komputasi dan Inform., vol. 5, no. 2, pp. 153–163, 2021, doi: https://doi.org/10.31603/komtika.v5i2.6280 Received:
A. Maizaliyanti, K. Umam, W. D. Yuniarti, and M. R. Handayani, “Implementasi Algoritma Random Forest dalam Klasifikasi Ulasan Pengunjung Mall Semarang untuk Pengambilan Keputusan Layanan,” Edumatic J. Pendidik. Inform., vol. 9, no. 2, pp. 452–461, 2025, doi: 10.29408/edumatic.v9i2.30379.
P. W. Rahayu et al., Buku Ajar Data Mining. Jambi: PT. Sonpedia Publising Indonesia, 2024.
Gishella Septania Al-Husna, Dian Asmarajati, Iman Ahmad Ihsannuddin, and Rina Mahmudati, “Perbandingan Metode Naïve Bayes Dan Support Vector Machine Untuk Analisis Sentimen Pada Ulasan Pengguna Aplikasi Linkedin,” STORAGE J. Ilm. Tek. dan Ilmu Komput., vol. 3, no. 2, pp. 139–144, 2024, doi: 10.55123/storage.v3i2.3602.
S. Widodo and B. Hartono, “Analisis Sentimen Pengguna Google Terhadap Destinasi Wisata Di Kota Semarang Menggunakan Metode K-Nearest Neighbor,” Progresif J. Ilm. Komput., vol. 19, no. 2, p. 545, 2023, doi: 10.35889/progresif.v19i2.1364.
D. K. Sumartha, “Implementasi IndoBert Untuk Analisis Sentimen Opini Publik Terhadap Kebijakan Kenaikan UKT Di Era Pemerintahan,” J. Inform. Dan Tek. Elektro Terap., vol. 13, no. 3, p. 9, 2025, doi: dx.doi.org/10.23960.
O. I. Gifari, M. Adha, F. Freddy, and F. F. S. Durrand, “Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine,” J. Inf. Technol., vol. 2, no. 1, pp. 36–40, 2022, doi: 10.46229/jifotech.v2i1.330.
C. H. P. 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.
D. Rudini, D. G. Purnama, and A. A. Khan, “Penggunaan Teknik Web Scraping Dalam Aplikasi Pengambilan Data Dari Google Maps Untuk Menunjang Digital Marketing,” Lentera Multidiscip. Stud., vol. 2, no. 1, pp. 10–19, 2023, doi: https://doi.org/10.57096/lentera.v2i1.61.
A. E. Perkasa and A. N. Putri, “Penerapan Naïve Bayes Untuk Analisis Sentimen Pada Ulasan Aplikasi Mobile Legends,” vol. 6, no. 4, 2025, doi: 10.47065/bits.v6i4.6507.
H. C. Husada and A. S. Paramita, “Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM),” vol. 10, no. 1, pp. 18–26, 2021, doi: 10.34148/teknika.v10i1.311.
T. Ridwansyah, “Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 2, no. 5, pp. 178–185, 2022, doi: https://doi.org/10.30865/klik.v2i5.362.
D. Anggraini and E. I. Wahyuni, “Analisis Sentimen Pengguna Terhadap Aplikasi Glints Di Google Play Store Menggunakan Metode Naive Bayes,” J. MEDIA Akad., vol. 3, no. 5, pp. 3031–5220, 2025, doi: https://doi.org/10.62281/v3i5.1903.
D. A. Tarigan, Z. Situmorang, and R. Rosnelly, “Analisis Sentimen Aplikasi Playstore Sirekap 2024 Pasca Pilpres Dengan Perbandingan Metode Support Vector Machine (SVM), Naive Bayes Classifier Dan Random Forest,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 3, pp. 661–670, 2025, doi: https://doi.org/10.25126/jtiik.2025129608.
E. M. Windy Livia Azzahra, “Analisis Sentimen Terhadap RSUD Salatiga Menggunakan SVM Dan TF-IDF,” vol. 6, no. 1, pp. 478–489, 2025, doi: https://doi.org.10.35870/jimik.v6i1.1208.
N. Wiliani, N. L. Chusna, and P. B. Ramadhan, Analisis Sentimen terhadap Pro Kontra Aksi Unjuk Rasa Mahasiswa dengan Naïve Bayes dan Information Gain. Pekalongan: Penerbit NEM, 2023.
I. S. Aisah, B. Irawan, and T. Suprapti, “Algoritma Support Vector Machine (SVM) Untuk Analisis Sentimen Ulasan Aplikasi Al Qur’an Digital,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 6, pp. 3759–3765, 2023, doi: https://doi.org/10.36040/jati.v7i6.8263.
G. Maulani et al., Machine Learning. Sumedang: CV. Mega Press Nusantara, 2025.
D. Wijaya, R. A. Saputra, and F. Irwiensyah, “Analisis Sentimen Ulasan Aplikasi Samsat Digital Nasional Pada Google Playstore Menggunakan Algoritma Naïve Bayes,” vol. 4, no. 4, 2024, doi: 10.30865/klik.v4i4.1738.
H. Setyawan, M. Yusril, P. Herwanto, and T. Wiharko, Machine Learning: Memahami Multinomial, Distribusi Probabilitas & Multinomial Naive Bayes. Kupang: Tangguh Denara Jaya Publisher, 2025.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Naïve Bayes dan Support Vector Machine Dalam Analisis Sentimen Google Maps Pusat Perbelanjaan
Pages: 27-38
Copyright (c) 2026 Eliza Cahyaningrum, Astrid Novita Putri
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
