Chatbot Model Development Using BERT for West Sumatera Halal Tourism Information
DOI:
https://doi.org/10.12962/j22759970.v4i2.1819Keywords:
chatbot, bert, natural language processing, halal tourism, west sumateraAbstract
Halal tourism in Indonesia is growing rapidly, highlighting the need for Muslim halal tourism information that gives unique and relevant information for traveller. However, providing timely and reliable information, specifically related to halal tourism remains a challenge. This research aims to address this by developing a chatbot model using BERT for West Sumatra’s halal tourism. A total of 1,125 questions were prepared, divided into nine categories or labels with 125 questions each. Eighty percent (900 questions) was used to fine-tune the BERT-base-multilingual-uncased model, while 20% (225 questions) was used for evaluation. The model was fine-tuned using BertForSequenceClassification for three epochs with a batch size of 32. The chatbot demonstrated high performance, with an overall accuracy of 0.96. However, the lowest precision value was 0.89 for “budaya” (or culture) and “kuliner” (or culinary) labels, and the lowest recall value was 0.64 for the “belanja” (or shopping) label, yielding an F1-score of 0.78. This study describes chatbot model development, from data collection and pre-processing to experimental setup and model training using a fine-tuned BERT-base-multilingual-uncased model. The chatbot model can group user queries into specific purposes and respond to a predefined list. However, one label (e.g “belanja” or shopping) may have the lowest recall due to a poor training dataset and query variation.
References
Redaksi, Indonesia Kembali Raih Predikat Top Muslim Friendly Destination of The Year 2024 Versi GMTI, Amphuri. https://amphuri.org/indonesia-kembali-raih-predikat-top-muslim-friendly-destination-of-the-year-2024-versi-gmti/ (accessed June 25, 2024).
B. Pusat Statistik, Wisatawan Sumatera Barat Tahun 2023. https://sumbar.bps.go.id/indicator/16/765/1/jumlah-kunjungan-wisatawan-nusantara-kabupaten-kota-di-provinsi-sumatera-barat.html (accessed April 28, 2024).
L. Lusiana, M. Neldi, and S. Sanjaya, Analisis Investasi Sektor Pariwisata, Jumlah Objek Wisata, Jumlah Kunjungan Wisatawan Dan Retribusi Kawasan Wisata Terhadap Pendapatan Asli Daerah Di Kota Padang, Jurnal Riset Akuntansi dan Keuangan. 9, No. 1 (2021) 25–34. doi: 10.17509/jrak.v9i1.28964.
I. Abashidze, The Influence of Online Platforms on Decision-Making Process and Behavioural Traits of International Travelers, European Scientific Institute, ESJ. 20, No. 37 (2024) 51. doi: 10.19044/esj.2024.v20n37p51.
M. Swanson, This Is How Much Time Travelers Spend Researching Before Booking a Trip, Travel Wires. https://www.travelwires.com/this-is-how-much-time-travelers-spend-researching-before-booking-a-trip (accessed July 31, 2024).
M. R. Harisi and E. M. Hiwono, Pengaruh Chatbot Usage terhadap Customer Satisfaction, Jurnal Ilmiah Manajemen Ekonomi Dan Akuntansi. 1, No. 2 (2024) 66–73. doi: 10.62017/jimea.v1i2.338.
U. Suyatman, Ruminda, and I. Yatmika, Pengembangan Muslim Friendly Tourism dalam Konsep Pariwisata Budaya di Pulau Dewata, Pusat Penelitian dan Penerbitan UIN Sunan Gunung Djati Bandung. (2019).
L. Pusvisasari, Tafsair Ahkam tentang Pariwisata Syariah, Jurnal Peradaban dan Hukum Islam, 3, No. 1 (2020) 39–58. doi: 10.29313/tahkim.v3i1.5642.
N. A. Noviyani and R. T. Ratnasari, Pengaruh Halal Destination Attributes di Sumatera Barat terhadap Wisatawan Muslim, Jurnal Ekonomi Syariah Teori dan Terapan. 8, No. 4 (2021) 401–412. doi: 10.20473/vol8iss20214pp401-412.
R. S, Exploring Halal Tourism: Current Trends and Future Directions, Journal of Islamic Marketing. 11, No. 4 (2020) 920–932.
Mastercard & Crescentrating, The Global Muslim Travel Index 2024, 9th ed. 2024.
A. Shamshiri, K. R. Ryu, and J. Y. Park, Text mining and natural language processing in construction, Automation in Construction. 158 (2024) 105200. doi: 10.1016/j.autcon.2023.105200.
J. Einstein, Introduction to Natural Language Processing. MIT Press, 2019.
A. Magableh, Context-Free Grammar for Aspect-Oriented UML Design Modeling Diagrams, Journal of Theoretical and Applied Information Technology. 97 No. 22 (2019) 3377–3385.
P. Johri, S. K. Khatri, A. T. Al-Taani, M. Sabharwal, Suvanov, and A. Kumar, Natural Language Processing: History, Evolution, Application, and Future Work, in Proceedings of 3rd International Conference on Computing Informatics and Networks, A. Abraham, O. Castillo, and D. Virmani, Eds., Singapore: Springer Singapore. 167 (2021) 365–375. doi: 10.1007/978-981-15-9712-1_31
D. McAllester and A. Brown, A Maximum Entropy Approach to the Hidden Markov Model, in Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL 2003), Association for Computational Linguistics. (2003) 154–161.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Cornell University. (2019) doi: 10.48550/arXiv.1810.04805.
R. Montti, Google’s BERT Rolls Out Worldwide, Search Engine Journal. https://www.searchenginejournal.com/google-bert-rolls-out-worldwide/339359/ (accessed June 29, 2024)
E. Adamopoulou and L. Moussiades, Chatbots: History, technology, and applications, Machine Learning with Applications. 2 (2020) 100006. doi: 10.1016/j.mlwa.2020.100006.
K. Hines, History Of ChatGPT: A Timeline Of The Meteoric Rise Of Generative AI Chatbots, Search Engine Journal. https://www.searchenginejournal.com/history-of-chatgpt-timeline/488370/ (accessed June 29, 2024).
H. J, Doherty Threshold, Laws of UX. https://lawsofux.com/doherty-threshold/ (accessed June 29, 2024).
I. Pereyra, Universal Principles of UX - 100 Timeless Strategies to Create Positive Interactions between People and Technology. Rockport Publishers, 2023.
Hugging Face, BERT multilingual base model (uncased). 2024. https://huggingface.co/google-bert/bert-base-multilingual-uncased (accessed June 29, 2024).
Hugging Face, BertForSequenceClassification. 2024. https://huggingface.co/docs/transformers/v4.41.3/en/model_doc/bert#transformers.BertForSequenceClassification (accessed June 29, 2024).
S. Aloria, I. Aggarwal, N. Baliyan, and M. Ghosh, Hilarious or Hidden? Detecting Sarcasm in Hinglish Tweets using BERT-GRU, in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). (2023) 1–6. doi: 10.1109/ICCCNT56998.2023.10307171.
G. Manias, A. Mavrogiorgou, A. Kiourtis, C. Symvoulidis, and D. Kyriazis, Multilingual text categorization and sentiment analysis: a comparative analysis of the utilization of multilingual approaches for classifying twitter data, Neural Comput & Applic. 35, No. 29 (2023) 21415–21431. doi: 10.1007/s00521-023-08629-3.
P. Keung, Y. Lu, G. Szarvas, and N. A. Smith, The Multilingual Amazon Reviews Corpus, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), B. Webber, T. Cohn, Y. He, and Y. Liu, Eds., Online: Association for Computational Linguistics. (2020) 4563–4568. doi: 10.18653/v1/2020.emnlp-main.369.
![](https://journal.its.ac.id/public/journals/2/submission_1819_1820_coverImage_en_US.png)
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Irmasari Hafidz, Bayu Siddhi Mukti, Qudsiyah Zahra Ilham Naseela, Ahmadhian Daffa Yudistira, I Putu Adhitya Pratama Mangku Purnama, Nurul Fajrin Ariyani, Hanim Maria Astuti, Aris Tjahyanto
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.