Analysis of Public Perceptions of Tertiary Hospitals Through Google Maps Using Naïve Bayes and Wordcloud
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Abstract
The study investigates the public perception of a tertiary hospital through Google Maps digital reviews using a Naïve Bayes approach and wordcloud. The data was collected by manual scraping technique from 2024-2025 and processed through preprocessing stages including cleansing, case folding, stopword removal, tokenizing, dan filter by legth. The data was then weighted by the TF-IDF technique before sentiment modeling by Naïve Bayes Classification. All of the processes use the Rapid Miner application. From 830 reviews collected, the accuracy of the Naïve Bayes model is 72,29% with precision 78,94%, recall 72,28%, and F1 score 74,76%. The results show that the dominant reviews are positive and describe the hospital health service as excellent. Therefore, the negative review shows that the hospital needs to improve its management in the pharmacy sector, administration, and patient queue. Neutral reviews tend to be descriptive without showing strong sentiments.
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References
Andriani, R., Andriani, P., & Andriani, D. (2023). Company performance improvement: implementation of service culture through human capital in hospitality industry. INOVASI: Jurnal Ekonomi, Keuangan, Dan Manajemen, 19(1), 10–16.
Andriani, R., Veranita, M., Noor, C. M., Ismail, K., Fauzzia, W., Hidayat, F. A., & Parino, N. H. S. (2022). Strategi Peningkatan Pemasaran Melalui Digital Marketing Pada Umkm Binangkit Kabupaten Bandung. JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia), 2(3), 356–359.
Ahmad, A., Sakidin, H., Sari, M. Y. A., Amin, A. R. M., Sufahani, S. F., & Rasib, A. W. (2021). Naïve Bayes Classification of High-Resolution Aerial Imagery. (IJACSA) International Journal of Advanced Computer Science and Applications, 12(11), 168–177.
Angel, A. C. T., Pranatawijaya, V. H., & Widiatry, W. (2024). Analisis Sentimen dan Emosi dari Ulasan Google Maps Untuk Layanan Rumah Sakit di Palangka Raya Menggunakan Machine Learning. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 4(1), 35–49. https://doi.org/10.24002/konstelasi.v4i1.8924
Darwis, D., Siskawati, N., & Abidin, Z. (2021). Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional. Jurnal TEKNO KOMPAK, 15(1), 131–135. https://doi.org/https://doi.org/10.33365/jtk.v15i1.744
Darzi, M. A., Islam, S. B., Khursheed, S. O., & Bhat, S. A. (2023). Service quality in the healthcare sector: a systematic review and meta-analysis. LBS Journal of Management & Research, 21(1), 13–29. https://doi.org/10.1108/LBSJMR-06-2022-0025
Elizar, C., Indrawati, R., & Syah, T. Y. R. (2020). Service Quality, Customer Satisfaction, Customer Trust, and Customer Loyalty in Service of Paediatric Polyclinic Over Private H Hospital of East Jakarta, Indonesia. Journal of Multidisciplinary Academic, 4(2).
Firmansyah, Y., Kurniawan, R., & Wijaya, Y. A. (2024). Analisis Data Sentimen Pemain Game Role-Playing Game (RPG) Honkai Star Rail dengan Algoritma Naive Bayes. Jurnal Informatika Dan Rekayasa Perangkat Lunak, 6(1), 127–135. https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/download/4343/799
Firmansyah, Z., & Puspitasari, N. F. (2021). Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Berdasarkan Opini Pada Twitter Menggunakan Algoritma Naive Bayes. Jurnal Teknik Informatika, 14(1), 171–178. https://doi.org/https://doi.org/10.15408/jti.v14i2.24024
Gunawan, B., Pratiwi, H. S., & Pratama, E. E. (2018). Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. JEPIN (Jurnal Edukasi Dan Penelitian Informatika), 4(2), 113–118.
Huwaida, S. F., Kusumawati, R., & Isnaini, B. (2024). Analisis sentimen komentar youtube terhadap pemindahan ibu kota negara menggunakan metode Naïve Bayes. Jambura Journal of Informatics, 6(1), 26–39. https://doi.org/https://doi.org/10.37905/jji.v6i1.24718
Insan, M. K., Hayati, U., & Nurdiawan, O. (2023). Analisis Sentimen Aplikasi Brimo Pada Ulasan Pengguna Di Google Play Menggunakan Algoritma Naive Bayes. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 478–483. https://doi.org/10.36040/jati.v7i1.6373
Ismail, A. R., & Hakim, R. B. F. (2023). Implementasi Lexicon Based Untuk Analisis Sentimen Dalam Mengetahui Trend Wisata Pantai Di DI Yogyakarta Berdasarkan Data Twitter. Emerging Statistics and Data Science Journal, 1(1), 37–46. https://doi.org/https://doi.org/10.20885/esds.vol1.iss.1.art5
Kusuma Wardani, S., & Arum Sari, Y. (2021). Analisis Sentimen menggunakan Metode Naïve Bayes Classifier terhadap Review Produk Perawatan Kulit Wajah menggunakan Seleksi Fitur N-gram dan Document Frequency Thresholding (Vol. 5, Issue 12). http://j-ptiik.ub.ac.id
Lestari, S., & Saepudin, S. (2021). Analisis Sentimen Vaksin Sinovac pada Twitter Menggunakan Algoritma Naïve Bayes. SISMATIK (Seminar Nasional Sistem Informasi Dan Manajemen Informatika) , 1(7), 163–170. https://sismatik.nusaputra.ac.id/index.php/sismatik/article/view/23/20
Limbong, J. J. A., Sembiring, I., & Hartomo, K. D. (2022). Analisis Klasifikasi Sentimen Ulasan Pada E-Commerce Shopee Berbasis Word Cloud Dengan Metode Naive Bayes Dan K-Nearest Neighbor. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 9(2), 347–356.
Makarim, Z., Nawangsih, I., & Sanudin, S. (2024). Naive Bayes Algorithm for Sentiment Analysis on Spider-Man Movie: No Way Home. Journal of Computer Networks, Architecture and High Performance Computing, 6(4), 1886–1897. https://doi.org/10.47709/cnahpc.v6i4.4845
Mikhael, L. (2022). Sosialisasi Pemahaman Larangan Undang-Undang Informasi Dan Transaksi Elektronik Serta Etika Penggunaan Media Sosial Pada Remaja. AIWADTHU: Jurnal Pengabdian Hukum, 2(2), 50. https://doi.org/10.47268/aiwadthu.v2i2.940
Nugraha, A. L. (2017). Pendeteksian Ambiguitas Makna Kata Untuk Meningkatkan Performa Analisa Sentimen Dengan Menggunakan Algoritma Similaritas Path Length. Universitas Muhammadyah Surakarta.
Putri, K. S., Setiawan, I. R., & Pambudi, A. (2024). Analisis Sentimen Terhadap Brand Skincare Lokal Menggunakan Naïve Bayes Classifier. Technologia, 14(3), 227–232. https://doi.org/http://dx.doi.org/10.31602/tji.v14i3.11259
Rachmawati, E., Yuyun Umniyatun, Deni Wahyudi Kurniawan, Mochamad Iqbal Nurmansyah, Mukhaer Pakkanna, Husnan Nurjuman, Slamet Budiarto, & Virgo Sulianto Gohardi. (2024). Analysis Of The Market Structure Of Hospital Industry In Indonesia. Jurnal Administrasi Kesehatan Indonesia, 12(1), 37–48. https://doi.org/10.20473/jaki.v12i1.2024.37-48
Rizki, A. F., Prihartono, W., & Rohman, F. (2025). Analisis Sentimen Ulasan Google Maps Rumah Sakit Khalishah di Cirebon dengan Algoritma Naive Bayes. Jurnal Informatika Dan Teknik Elektro Terapan, 13(2), 418–427. https://doi.org/10.23960/jitet.v13i2.6309
Ruffer, N., Knitza, J., & Krusche, M. (2020). #Covid4Rheum: an analytical twitter study in the time of the COVID-19 pandemic. Rheumatology International, 40(12), 2031–2037. https://doi.org/10.1007/s00296-020-04710-5
Safira, A., & Hasan, F. N. (2023). Analisis Sentimen Masyarakat Terhadap Paylater Menggunakan Metode Naive Bayes. Jurnal Sistem Informasi, 5(1), 59–70. https://doi.org/https://doi.org/10.31849/zn.v5i1.12856
Salsabila, N. A. (2022). Analisis Sentimen Pada Media Sosial Twitter Terhadap Tokoh Gus Dur Menggunakan Metode Naïve Bayes dan Support Vector Machine (SVM). In Universitas Islam Negeri Syarif Hidayatullah. Universitas Islam Negeri Syarif Hidayatullah. https://repository.uinjkt.ac.id/dspace/handle/123456789/66253
Saragih, H. M. (2021). Analisis Sentimen Pengguna Twitter Terhadap Layanan Pajak Kendaraan Bermotor Menggunakan Algoritme Naive Bayes Classifier. In Universitas Lampung. Universitas Lampung.
Sendi, A. R., Alam, S., & Kurniawan, I. (2023). Analisis Sentimen Pengguna Aplikasi Jmo (Jamsostek Mobile) Pada Google Play Store Menggunakan Metode Naive Bayes. 2(3), 109–117. https://doi.org/https://doi.org/10.55123/storage.v2i3.2334
Stout, D. W. (2025, February 21). Weighted Metrics for Multi-Class Models Explained. Magai. https://magai.co/weighted-metrics-for-multi-class-models-explained/
Suhadi, E. (2025). Analisis Sentimen Aplikasi Bisa Ekspor Pada Ulasan Pengguna Di Google Play Dengan Naïve Bayes. JIKA (Jurnal Informatika) Universitas Muhammadiyah Tangerang, 9(1), 93–101.
Soesilo, D., Rohendi, A., Hidayat, D., & Syaodih, E. (2024). Efektivitas Pemasaran Digital, Citra Merek, Variabel Intervening Kepercayaan Pasien Pada Rsgmp Nala Husada Surabaya. Journal of Innovation Research and Knowledge, 4(6), 3807–3816.
Trista, R. T., & Asmoro, E. T. (2025). Analisis Sentimen dalam Data Big Data (Studi Kasus pada Media Sosial). Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research , 2(1), 700–706. https://doi.org/https://doi.org/10.32672/mister.v2i1.2518
Utomo, M. A., Purwadhi, P., & Hidayat, D. (2023). Pengaruh Manajemen Sarana Kesehatan Terhadap Kepuasan Pasien Di Klinik Pratama Ptpn Vii Kantor Direksi Bandar Lampung. Referensi : Jurnal Ilmu Manajemen Dan Akuntansi, 11(2), 107–116. https://doi.org/10.33366/ref.v11i2.4641
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1
Wardani, N. R., & Erfina, A. (2021). Analisis Sentimen Masyarakat Terhadap Layanan Konsultasi Dokter Menggunakan Algoritma Naive Bayes. SISMATIK (Seminar Nasional Sistem Informasi Dan Manajemen Informatika), 11–18.
Yassir, A., Purwadhi, P., & Andriani, R. (2023). Hubungan mutu pelayanan terhadap minat kunjungan ulang pasien di klinik citra medika kota Semarang. Jurnal Riset Pendidikan Ekonomi, 8(1), 1–12. https://doi.org/10.21067/jrpe.v8i1.8239
Zamzami, F., Hidayat, R., & Fathonah, R. (2024). Penerapan Algoritma Naive Bayes Classifier Untuk Analisis Sentimen Komentar Twitter Proyek Pembagunan IKN. Faktor Exacta, 17(1). https://doi.org/10.30998/faktorexacta.v17i1.22265