Analisis Sentimen Penentuan Prioritas Perbaikan Layanan Museum Provinsi Sumatera Utara Berdasarkan Ulasan Pengunjung Menggunakan Metode Naïve Bayes

Annisa Tasya Fadila; Wilda Rina Hasibuan

doi:10.33151/ijomss.v4i6.1126

Annisa Tasya Fadila Universitas Muhammadiyah Sumatera Utara
Wilda Rina Hasibuan Universitas Muhammadiyah Sumatera Utara

DOI: https://doi.org/10.33151/ijomss.v4i6.1126

Keywords: Analisis Sentimen, Naïve Bayes, TF-IDF, Web Scraping, Museum Provinsi Sumatera Utara

Abstract

Museum Provinsi Sumatera Utara menerima ulasan pengunjung secara terus-menerus melalui platform Google Maps, namun proses analisis ulasan secara manual tidak efektif akibat besarnya volume data. Penelitian ini bertujuan membangun sistem analisis sentimen berbasis web yang mampu mengklasifikasikan ulasan pengunjung secara otomatis ke dalam kategori positif, negatif, dan netral, serta menentukan prioritas perbaikan layanan berdasarkan hasil analisis tersebut. Data sebanyak 500 ulasan dikumpulkan menggunakan teknik web scraping melalui platform Apify. Data kemudian diproses melalui tahapan preprocessing teks yang meliputi case folding, cleaning, tokenizing, stopword removal, dan stemming, dilanjutkan dengan pembobotan fitur menggunakan TF-IDF (Term Frequency–Inverse Document Frequency) dan klasifikasi menggunakan algoritma Multinomial Naïve Bayes. Pelabelan sentimen dilakukan berdasarkan nilai rating pengunjung, yaitu rating 4–5 sebagai positif, rating 3 sebagai netral, dan rating 1–2 sebagai negatif. Hasil klasifikasi menggunakan data latih 80% dan data uji 20% menunjukkan akurasi sebesar 88,0%. Dari keseluruhan 500 ulasan, diperoleh 441 ulasan (88,2%) bersentimen positif, 28 ulasan (5,6%) netral, dan 31 ulasan (6,2%) negatif. Analisis aspek layanan berbasis keyword matching terhadap lima aspek—fasilitas, pelayanan, koleksi museum, kebersihan, dan aksesibilitas—menunjukkan bahwa aspek fasilitas dan pelayanan memperoleh jumlah sentimen negatif tertinggi masing-masing delapan ulasan sehingga menjadi prioritas utama perbaikan. Sistem yang dibangun telah diuji menggunakan metode Black Box Testing dan seluruh fungsi berjalan sesuai yang diharapkan. Hasil penelitian ini memberikan rekomendasi berbasis data bagi pengelola museum untuk meningkatkan kualitas layanan secara lebih terarah dan efektif.

Author Biographies

Annisa Tasya Fadila, Universitas Muhammadiyah Sumatera Utara

Program Studi Sistem Informasi, Fakultas Ilmu Komputer dan Teknologi Informasi

Wilda Rina Hasibuan, Universitas Muhammadiyah Sumatera Utara

Program Studi Sistem Informasi, Fakultas Ilmu Komputer dan Teknologi Informasi

References

Afrad, M., Febrianto, D. C., Wijayanto, S., & Fathoni, M. Y. (2024). Sentiment Analysis of Visitor Reviews on Baturaden Tourist Attraction Using Machine Learning Methods. Jurnal Ilmiah Teknologi dan Rekayasa, 11(128), 57–64.

Berrar, D. (2021). Bayes' Theorem and Naive Bayes Classifier. Encyclopedia of Bioinformatics and Computational Biology, 1, 403–412. https://doi.org/10.1016/B978-0-12-809633-8.20473-1

Chrisinta, D., & Simarmata, J. E. (2024). Eksplorasi Teknik Web Scraping pada Data Mining: Pendekatan Pencarian Data Berbasis Python. Faktor Exacta, 17(1), 58–68. https://doi.org/10.30998/faktorexacta.v17i1.22393

Dodiya, T. (2021). Using Term Frequency - Inverse Document Frequency to find the Relevance of Words in Gujarati Language. International Journal for Research in Applied Science and Engineering Technology, 9(4), 378–381. https://doi.org/10.22214/ijraset.2021.33625

Fatah, D. A., Mala, E., Rochman, S., Setiawan, W., Aulia, A. R., Kamil, F. I., & Susanto, A. (2024). Sentiment Analysis of Public Opinion Towards Tourism in Bangkalan Regency Using Naïve Bayes Method. IOP Conference Series: Earth and Environmental Science, 01016, 1–8.

Feng, X., Wang, C., & Zou, T. T. (2024). Visitor Experience of the Grand Canal National Cultural Park Museum Based on Sentiment Analysis Algorithm. Heritage Science, 11(9), 142–150.

Gentzel, P., & Wimmer, J. (2024). Restricted but satisfied: Google Maps and agency in the mundane life. Convergence: The International Journal of Research into New Media Technologies, 30(3), 1145–1161. https://doi.org/10.1177/13548565231205869

Hamdana, E. N., Okta, A., Wardani, N., Retno, A., & Hayati, T. (2025). Sentiment Analysis of Visitor Reviews on Google Maps at Kampung Coklat Tourism. Jurnal Ilmiah Informatika dan Sistem Informasi Edukasi (JAISE), 5(1), 274–282. https://doi.org/10.30811/jaise.v5i1.6488

Hassani, H., Beneki, C., Unger, S., & Mazinani, M. T. (2020). Text Mining in Big Data Analytics. Big Data and Cognitive Computing, 4(1), 1–34. https://doi.org/10.3390/bdcc4010001

Huo, H., Shin, K. S., Han, C., & Yang, M. (2024). Measuring the relationship between museum attributes and visitors: An application of topic model on museum online reviews. PLOS ONE, 19(6), e0304901. https://doi.org/10.1371/journal.pone.0304901

Jin, Y., Cheng, K., Wang, X., & Cai, L. (2023). A Review of Text Sentiment Analysis Methods and Applications. Electronics, 12(18), 3829. https://doi.org/10.3390/electronics12183829

Kulkarni, S., Buradkar, A., Ghadge, P., & Khainar, S. (2023). Web Scraping: Extracting Insights from the Digital Landscape. International Journal of Advanced Research in Science, Communication and Technology, 3(2), 102–108.

Lata, K., Sood, A., Kaur, K., Benipal, A. K., & Pateriya, B. (2022). Web-GIS based Dashboard for Real-Time Data Visualization & Analysis using Open Source Technologies. International Journal of Advanced Computer Science and Applications (IJACSA), 16(2), 134–145.

Laudza, H. A., Putra, J. T., Atstsaqif, M. D., & Setiawan, I. K. (2025). Sentiment Analysis and Topic Modelling of Tourist Reviews on Heritage Destinations of Lawang Sewu. International Journal of Advanced Science and Research Excellence (IJASRE), 11(9), 38–48. https://doi.org/10.31695/IJASRE.2025.9.3

Liu, B. (2021). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.

Mao, Y., Liu, Q., & Zhang, Y. (2024). Sentiment analysis methods, applications, and challenges: A systematic literature review. Journal of King Saud University – Computer and Information Sciences, 36(4), 102048. https://doi.org/10.1016/j.jksuci.2024.102048

McCallum, A., & Nigam, K. (1998). A comparison of event models for naive Bayes text classification. AAAI-98 Workshop on Learning for Text Categorization. AAAI Press.

Pangestu, Y., & Basri, M. (2024). Analisis Perbandingan Multinomial Naïve Bayes dan Adaboost dalam Mengklasifikasikan Sentimen Terkait Pinjaman Online. Jurnal Ilmu Komputer dan Informatika (JIKi), 9(2), 373–384.

Perwira, R. I., & Permadi, V. A. (2025). Domain-Specific Fine-Tuning of IndoBERT for Aspect-Based Sentiment Analysis in Indonesian Travel User-Generated Content. Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 11(1), 30–40.

Qaiser, S., & Ali, R. (2021). Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents. International Journal of Computer Applications, 181(1), 25–29. https://doi.org/10.5120/ijca2018917395

Rezaeian, N., & Novikova, G. (2020). Persian text classification using naive Bayes algorithms and support vector machine algorithm. Indonesian Journal of Electrical Engineering and Informatics, 8(1), 178–188. https://doi.org/10.11591/ijeei.v8i1.1696

Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023). Sentiment analysis and opinion mining on educational data: A survey. Natural Language Processing Journal, 2, 100003. https://doi.org/10.1016/j.nlp.2022.100003

Singgalen, Y. A. (2022). Analisis Sentimen Wisatawan Melalui Data Ulasan Candi Borobudur di Tripadvisor Menggunakan Algoritma Naïve Bayes Classifier. BITS: Jurnal Teknologi Informasi dan Sistem Informasi, 4(3), 1–12. https://doi.org/10.47065/bits.v4i3.2486

Singh, G., Kumar, B., Gaur, L., & Tyagi, A. (2019). Comparison between Multinomial and Bernoulli Naïve Bayes for Text Classification. Proceedings of the 2019 International Conference on Automation, Computational and Technology Management (ICACTM). https://doi.org/10.1109/ICACTM.2019.8776800

Tarmizi, N. H., Iman, N., Husna, A., Isa, N., & Yusoff, S. (2024). InsightVista: Unveiling Visitor Sentiments and Trends for Terengganu State Museum Using Text Analytics. Proceedings of the 19th International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2024), 87–95.

Woo, J. S., Suslow, P., Thorsen, R., Ma, R., Bakhtary, S., Moayeri, M., & Nambiar, A. (2019). Development and Implementation of Real-Time Web-Based Dashboards in a Multisite Transfusion Service. Journal of Pathology Informatics, 10(1), 3. https://doi.org/10.4103/jpi.jpi_36_18

Xu, S. (2021). Bayesian Naïve Bayes classifiers to text classification. Journal of Information Science, 44(1), 48–59. https://doi.org/10.1177/0165551516677973

Zhang, H. (2004). The optimality of Naive Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004), 562–567.