Penerapan Model BERT pada Chatbot dalam Platform E-Commerce

Authors

DOI:

https://doi.org/10.30872/atasi.v4i1.3039

Keywords:

Chatshop, BERT, BLEU, ROUGE

Abstract

This study develops chatshop, a BERT-based e-commerce chatbot designed to assist customers in searching for pizza menus. The BERT model was chosen because of its ability to understand sentence context bidirectionally, thereby increasing the accuracy in detecting user intent. This chatshop allows users to find menus, get recommendations, and access price and stock availability information in real time. The evaluation was carried out using BLEU and ROUGE-L metrics, with a ROUGE-L F1 score of 32.91%. These results indicate that the chatbot is able to handle simple interactions well, but still needs improvement in answering more complex questions accurately and completely.

References

Singh, S., & Sai Vijay, T. (2024). Technology roadmapping for the e-commerce sector: A text-mining approach. Journal of Retailing and Consumer Services, 81, 103977. https://doi.org/10.1016/j.jretconser.2024.103977

Lu, Y., & Zhang, J. (2025). Balancing identity diversity and product contexts: Understanding consumer trust in AI-enhanced chatbot services. Journal of Retailing and Consumer Services, 84, 104205. https://doi.org/10.1016/j.jretconser.2024.104205

Wong, C. M., Feng, F., Zhang, W., Chen, H., Vong, C. M., & Chen, C. (2024). Billion-scale pre-trained knowledge graph model for conversational chatbot. Neurocomputing, 606, 128353. https://doi.org/10.1016/j.neucom.2024.128353

Ngai, E. W. T., Lee, M. C. M., Luo, M., Chan, P. S. L., & Liang, T. (2021). An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50, 101098. https://doi.org/10.1016/j.elerap.2021.101098

Sanjaya, W., Calvin, Muhammad, R., Meiliana, & Fajar, M. (2023). Systematic Literature Review on Implementation of Chatbots for Commerce Use. Procedia Computer Science, 227, 432–438. https://doi.org/10.1016/j.procs.2023.10.543

Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. K. (2024). LLMs in e-commerce: A comparative analysis of GPT and LLaMA models in product review evaluation. Natural Language Processing Journal, 6, 100056. https://doi.org/10.1016/j.nlp.2024.100056

Habbat, N., Nouri, H., Anoun, H., & Hassouni, L. (2023). Sentiment analysis of imbalanced datasets using BERT and ensemble stacking for deep learning. Engineering Applications of Artificial Intelligence, 126, 106999. https://doi.org/10.1016/j.engappai.2023.106999

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

Zhang, L., Sun, X., & Yu, Y. (2023). Personalized Chatbot System Using BERT in Online Food Ordering. IEEE Access, 11, 12983–12994. https://doi.org/10.1109/ACCESS.2023.3241123

Lin, T., Ma, J., & Liu, J. (2022). Model Compression Techniques for Deploying Chatbots on Mobile Platforms. Information Processing & Management, 59(6), 103069.

Qiu, M., Li, F., & Gao, Y. (2022). Multi-turn context-aware response generation in e-commerce chatbots. ACM Transactions on Information Systems, 40(4), 1–28.

Rahmat, M. A., Indrabayu, & Areni, I. S. (2019). Hoax web detection for news in Bahasa using support vector machine. In 2019 International Conference on Information and Communications Technology (ICOIACT) (pp. 332–336). IEEE. https://doi.org/10.1109/ICOIACT46704.2019.8938425

Wentzel, G. (1922). Funkenlinien im Röntgenspektrum. Annalen der Physik, 371(23), 437–461. https://doi.org/10.1002/andp.19223712302

Lin, C. Y. (2004). ROUGE: A package for automatic evaluation of summaries. In Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004) (pp. 25–26).

Brahi, A., Touahria, M., & Tari, A. (2019). Toward conversational recommendation systems: A comparative study. International Journal of Computer Applications, 177(38), 1–7. https://doi.org/10.5120/ijca2019919641

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Published

10-06-2025

How to Cite

Penerapan Model BERT pada Chatbot dalam Platform E-Commerce. (2025). Adopsi Teknologi Dan Sistem Informasi (ATASI), 4(1), 72-79. https://doi.org/10.30872/atasi.v4i1.3039