APPLICATION OF CONVERSATIONAL CHATBOT FOR BRAKE PADS BUSINESS IN THAILAND VIA LINE APPLICATION
24
Issued Date
2025-05-01
Resource Type
ISSN
21852766
Scopus ID
2-s2.0-105002673270
Journal Title
ICIC Express Letters, Part B: Applications
Volume
16
Issue
5
Start Page
559
End Page
565
Rights Holder(s)
SCOPUS
Bibliographic Citation
ICIC Express Letters, Part B: Applications Vol.16 No.5 (2025) , 559-565
Suggested Citation
Jitsangob S., Amornsamankul S., Hobanluekit B., Sriwiboon M., Unhapipat S. APPLICATION OF CONVERSATIONAL CHATBOT FOR BRAKE PADS BUSINESS IN THAILAND VIA LINE APPLICATION. ICIC Express Letters, Part B: Applications Vol.16 No.5 (2025) , 559-565. 565. doi:10.24507/icicelb.16.05.559 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/109652
Title
APPLICATION OF CONVERSATIONAL CHATBOT FOR BRAKE PADS BUSINESS IN THAILAND VIA LINE APPLICATION
Corresponding Author(s)
Other Contributor(s)
Abstract
In recent years, the automotive parts industry has witnessed a significant shift towards digital solutions to enhance customer engagement and streamline business operations. This study explores the implementation of a conversational chatbot tailored for the brake pads business in Thailand, utilizing the widely used Line application as the primary platform. The chatbot focuses on enhancing and developing the beta version of a Frequently Asked Questions chatbot (FAQs chatbot) using Dialogflow in the Line Official Account (Line OA) of a researched company in Thailand. The goal is to provide precise answers to customer queries, improving customer service and access to product information. The confusion matrix was used to evaluate the response performance of FAQs chatbots such as the accuracy of answers in terms of precision, recall, and F1-score. The proposed chatbot answered the users significantly correctly with precision, recall and F1-score reported as 0.95, 0.97, and 0.96, respectively. The accuracy of answering is equal to 0.93. As a result, the accuracy can be increased by the training chatbot in Dialogflow. Accordingly, more training phases were trained for the chatbot, and better performance of the chatbot was deserved.