in Chatbot-based Information Service using RASA Open-SourceFrameworkin Prambanan Temple Tourism Object

  • Zein Hanni Pradana Institut Teknologi Telkom Purwokerto
  • Hanin Nafi'ah Institut Teknologi Telkom Purwokerto
  • Raditya Artha Rochmanto Institut Teknologi Telkom Purwokerto
Keywords: Covid-19, Chatbot, Machine Learning, RASA Open Source, Prambanan Temple

Abstract

The pandemic has caused a shift in the tourism industry's drive towards comprehensive digitization. This approach is used to prevent the spread of the Covid-19 virus. The impact of Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) limiting the mobility of tourists who will vacation in Indonesia causes losses and foreign exchange earnings of the state in the tourism industry sector of 20.7 billion. So, to survive in the current situation, industry players must be able to adapt and rise by providing more effective innovations. This study aims to develop a Question Answering System or a digital question and answer system using a chatbot (ChatterBot). The chatbot is used as an information service provider that can make it easier for tourists who are looking for information about tourist attractions. Chatbot-based information service systems can work 24 hours or all day, reducing the intensity of direct physical contact with officers and saving operational costs. The chatbot implementation is built on the Machine Learning Framework using RASA Open Source with the Python programming language. The knowledge base of the chatbot system is trained based on the FAQ (Frequently Asking Question) dataset with a case study of the Prambanan Temple tourist attraction as a sample of Indonesian tourism. The results of the evaluation and system performance based on data testing obtained the level of model accuracy is 0.91. Furthermore, the weighted average value in the Confusion Matrix produces a precision of 0.97, a recall of 0.94, and an F1-score of 0.95. The training and testing model processes locally using the Visual Studio Code software.

 

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References

Kementerian Keuangan Republik Indonesia, Merekam Pandemi Covid-19 dan Memahami Kerja Keras Pengawal APBN. Jakarta: Kementerian Keuangan Republik Indonesia, 2021.

Balai Pelestarian Cagar Budaya, “Perpanjangan Penutupan Candi Prambanan,” 30 Juni, 2021. https://bpcbdiy.kemdikbud.go.id/berita-perpanjangan-penutupan-candi-prambanan (accessed Mar. 02, 2022).

B. P. Statistik, “Perkembangan Pariwisata Dan Transportasi Nasional,” Jakarta Badan Pus. Stat., no. 04, pp. 1–20, 2021.

Wonderful Indonesia, Trend Pariwisata 2021. Kemenparekraf Baparekra, 2020.

S. Sugiono, “Pemanfaatan Chatbot Pada Masa Pandemi Covid-19 : Kajian Fenomena Society 5 . 0,” vol. 22, no. 2, pp. 133–148, 2021.

A. Abdellatif, K. Badran, D. Costa, and E. Shihab, “A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering,” IEEE Trans. Softw. Eng., pp. 1–19, 2021, doi: 10.1109/TSE.2021.3078384.

R. K. Sharma and M. Joshi, “An Analytical Study and Review of open source Chatbot framework, Rasa,” Int. J. Eng. Res., vol. 9, no. 06, pp. 1011–1014, 2020.

M. R. S. dan F. Atqiya, “Sistem Tanya Jawab Konsultasi Shalat Berbasis RASA Natural Language Understanding ( NLU ),” vol. 3, no. 2, pp. 93–102, 2021, doi: 10.17509/edsence.v3i2.38732.

D. G. S. Ruindungan and A. Jacobus, “Pengembangan Chatbot untuk Layanan Informasi Interaktif Akademik menggunakan Framework Rasa Open Source,” vol. 10, no. 1, pp. 61–68, 2021.

M. D. Aldiansyah, “Keunikan Sejarah Candi Prambanan Yogyakarta,” 2019.

S. Studer et al., “Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology,” Mach. Learn. Knowl. Extr., vol. 3, no. 2, pp. 392–413, 2021, doi: 10.3390/make3020020.

RASA, “Training Data Format,” 2020. https://rasa.com/docs/rasa/training-data-format/#! (accessed Jan. 21, 2022).

RASA, “Model Configuration,” 2019. https://rasa.com/docs/rasa/model-configuration/ (accessed Jan. 22, 2022).

M. Grandini, E. Bagli, and G. Visani, “Metrics for Multi-Class Classification: an Overview,” pp. 1–17, 2020, [Online]. Available: http://arxiv.org/abs/2008.05756.

M. Khalusova, “Machine Learning Model Evaluation Metrics Part 2: Multi-Class Classification,” 17 April, 2019. https://www.mariakhalusova.com/posts/2019-04-17-ml-model-evaluation-metrics-p2/ (accessed Mar. 02, 2022).

Published
2022-08-31
How to Cite
Zein Hanni Pradana, Hanin Nafi’ah, & Raditya Artha Rochmanto. (2022). in Chatbot-based Information Service using RASA Open-SourceFrameworkin Prambanan Temple Tourism Object. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(4), 656 - 662. https://doi.org/10.29207/resti.v6i4.3913
Section
Artikel Teknologi Informasi