Naïve Bayes–Based Chatbot with Sentiment Analysis for Culinary Preferences in Bali
DOI:
10.33395/sinkron.v9i4.15291Keywords:
Chatbot, Google Maps reviews, Naïve Bayes, Sentiment analysis, User preferencesAbstract
The rapid growth of digital technology has increased the adoption of chatbots across industries, including the culinary and tourism sectors. However, existing systems often lack integration of customer sentiment and user preferences, limiting recommendation relevance. This study develops a personalized chatbot by combining sentiment analysis of Google Maps reviews with user taste preferences for traditional Balinese cuisine. A dataset of 5,000 reviews was analyzed using the Naïve Bayes classifier, achieving 88% accuracy. User evaluation with 100 respondents showed positive perceptions of usability and engagement, though recommendation suitability scored lower. The findings highlight the potential of sentiment-driven personalization and suggest future improvements through advanced models, larger datasets, and multilingual features for tourism.
Downloads
References
Atmadji, E. S. J., Wabula, Y., Karsanti, H. T., & Kristopher, K. (2025). The use of Naive Bayes Classifier in sentiment analysis at Indonesia’s super priority tourism destinations based on user reviews. Jurnal Sosioteknologi Institut Teknologi Bandung, 24(2), 226–239. https://doi.org/10.5614/sostek.itbj.2025.24.2.7
Budiman, S. N., Lestanti, S., & Erwan. (2024). Analisis sentimen berdasarkan hasil review lokasi Google Map menggunakan Natural Language Toolkit Textblob dan Naïve Bayes. JAMI: Jurnal Ahli Muda Indonesia, 5(2), 114–126. https://doi.org/10.46510/jami.v5i2.311
Dang, C. N., Moreno-garc, N., & De la Prieta, F. (2021). An approach to integrating sentiment analysis into recommender systems. Sensors, 21(16), 1–17. https://doi.org/10.3390/s21165666
Gilbert Darmawan, Syariful Alam, M. I. S. (2023). Analisis sentimen berdasarkan ulasan pengguna aplikasi Mypertamina pada Google Play Store menggunakan metode Naïve Bayes. Jurnal Ilmiah Teknik Dan Ilmu Komputer, 2(3), 100–108.
Ida Widaningrum, D. M., Arifin, R., Tsaqila, S. L., & Fatmawati, D. (2022). Algoritma Term Frequency – Inverse Document Frequency (TF-IDF) dan K-Means Clustering untuk menentukan kategori dokumen. Prosiding Seminar Nasional SISFOTEK, 145–149.
Karo, I. M. K., Karo, J. A. K., Djasmayena, S., & Wahyudi, R. (2025). Sentiment analysis of tourist reviews at Waterfront City Pangururan using Naive Bayes and TF-IDF algorithm. Journal of Software Engineering, Information and Communication Technology (SEICT), 6(1), 13–20. https://doi.org/10.17509/seict.v6i1.86210
Khoerunnisa, S., Shiddieq, D. F., & Dwi Nurhayati, D. (2025). Penerapan algoritma Naive Bayes dengan teknik TF-IDF dan Cross Validation untuk analisis sentimen terhadap Starlink. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 5(2), 566–577. https://doi.org/10.57152/malcom.v5i2.1852
Kosala N, N. V. (2025). Influence of pre-processing strategies on sentiment analysis performance: Leveraging Bert, TF-IDF and Glove Features. Journal of Machine and Computing, 5(1), 464–473. https://doi.org/10.53759/7669/jmc202505036
Kusnanda, I. G. R., Sukarsa, I. M., & Susila, A. A. N. H. (2023). Perancangan chatbot hotel dengan model Natural Language Processing Chatbot dan Button Based Chatbot. JITTER- Jurnal Ilmiah Teknologi Dan Komputer, 3(1).
Lin, C. H., & Nuha, U. (2023). Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy. Journal of Big Data Volume, 10(88), 1–19. https://doi.org/10.1186/s40537-023-00782-9
Masripah, S., & Utami, L. D. (2020). Algoritma klasifikasi Naive Bayes untuk analisa sentimen aplikasi Shopee. Jurnal SWABUM, 8(2), 114–117. https://doi.org/10.31294/swabumi.v8i2.8444
Mayla, H. M., & Nasution, M. I. P. (2023). Analisis peran chatbot dalam meningkatkan pelayanan terhadap konsumen di e-commerce. Kohesi: Jurnal Sains Dan Teknologi, 1(11), 71–80. https://doi.org/10.3785/kohesi.v1i11.1160
Muhamad Mustaqim, Ari Gunawan, Yudistira Bagus Pratama, I. Z. (2023). Pengembangan chatbot layanan publik menggunakan Machine Learning dan Natural Languange Processing. Journal of Information Technology and Society (JITS), 1(1), 1–4. https://doi.org/10.35438/jits.v1i1.16
Muttaqin, M. N., & Kharisudin, I. (2021). Analisis sentimen aplikasi Gojek menggunakan Support Vector Machine dan K Nearest Neighbor. UNNES Journal of Mathematics, 10(2), 23–27. https://doi.org/10.15294/ujm.v10i2.48474
Nagar, M., Kumar, C., & Puri, S. (2025). Optimizing sentiment analysis with Naïve Bayes and Random Forest Techniques: a result-based approach. Journal of Artificial Intelligence Research & Advances, 12(2).
OECD. (2024). Artificial Intelligence and tourism. https://www.oecd.org/en/publications/artificial-intelligence-and-tourism_3f9a4d8d-en.html%0Ahttp://dx.doi.org/10.1787/3f9a4d8d-en2025
Popmenu. (2024). AI in Restaurants: Why it’s booming, how it’s being used, and real-world results. https://get.popmenu.com/toolkit/ai-in-restaurants
Purnama, D. G., Djuanda, D. R., Mahendra, & Albart, N. (2023). Pengembangan aplikasi pencarian artikel ilmiah berbasis mobile. Technomedia Journal (TMJ), 8(2), 135–150. https://doi.org/10.33050/tmj.v8i2SP.2010
Rahayu, I. P., Fauzi, A., & Indra, J. (2022). Analisis sentimen terhadap program kampus merdeka menggunakan Naive Bayes dan Support Vector Machine. Jurnal Sistem Komputer Dan Informatika (JSON), 4(2), 296–301. https://doi.org/10.30865/json.v4i2.5381
Rizquina, A. Z., & Ratnasari, C. I. (2023). Implementasi web scraping untuk pengambilan data pada website e-commerce. Jurnal Teknologi Dan Sistem Informasi Bisnis, 5(4), 377–383. https://doi.org/10.47233/jteksis.v5i4.913
Safar, N. Z. M., Mujilahwati, S., Halif, K. M. N. K., & Ghazalli, N. (2024). Optimizing sentiment analysis of indonesian texts: enhancing Deep Learning Models with Genetic Algorithm-Based Feature Selection. Journal of Soft Computing and Data Mining (JSCDM), 5(2), 208–222. https://doi.org/10.30880/jscdm.2024.05.02.016
Utomo, P. E. P., Manaar, M., Khaira, U., & Suratno. (2019). Analisis sentimen online review pengguna Bukalapak menggunakan metode algoritma TF-IDF. Jurnal Sains Dan Sistem Informasi (JUSS), 2(2), 35–39. https://doi.org/10.22437/juss.v2i2.8469
Widhiyanti, A. A. S., & Sekarini, I. G. A. A. (2024). Enhancing customer interaction with chatbot in digital marketing for traditional Balinese food sellers. ARRUS Journal of Engineering and Technology, 4(2), 184–195. https://doi.org/10.35877/jetech3296
Yaqoob, S., Noor, A., Noor, T. H., Khan, M. Z., Ejaz, A., Alam, M. I., Rana, N., & Ejaz, K. (2025). Enhancing student career guidance and sentimental analysis: A performance-driven hybrid learning approach with feature ranking. PLoS ONE, 20(5 May), 1–27. https://doi.org/10.1371/journal.pone.0321108
Downloads
|
How to Cite
Issue
Section
License
Copyright (c) 2025 Anak Agung Sandatya Widhiyanti, I Gusti Agung Ayu Sekarini
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
Muhammad Khoiruddin harahap
