The Need for a Chatbot Platform

Oct 13, 2025 by sorusgentrification.com 121 views

Application of the TF-Idf and Deep Neural Network (DNN) Methods in the USU Fasilkom-TI Academic Information Service Chatbot

Introduction

In today's digital age, educational institutions play a crucial role in meeting the information needs of students. The availability of valid and easily accessible information is vital in the world of education, especially in the university environment. However, the current method of disseminating information through WhatsApp messages shared by KOMING has its limitations. This method makes students risk losing information, causes irregular information documentation, and poses time and distance constraints when students want to ask the campus. Therefore, a chatbot platform that can provide necessary information and answer students' questions easily, anytime, and anywhere is essential.

The Need for a Chatbot Platform

A chatbot platform can revolutionize the way students access information and interact with the campus. It can provide a centralized hub for students to ask questions, receive answers, and access relevant information. Moreover, a chatbot can help students navigate the campus, provide guidance on academic matters, and offer support services. In this study, the Terquency-Inverse Document Frequency (TF-IdF) and Deep Neural Network (DNN) methods were used to develop a chatbot that can meet the information needs of students at the USU Fasilkom-TI.

The TF-IdF Method

The TF-IdF method is a technique commonly used in processing natural languages to determine the importance of a word in the context of a particular document. In this chatbot case, TF-IdF helps in identifying relevant keywords of questions raised by students. By analyzing the frequency of the appearance of words and its distribution in existing documents, chatbots can find out which information is more significant. This is very useful for building relevant knowledge bases and helping chatbots provide the right answer.

The DNN Method

Deep Neural Network (DNN) is one type of artificial intelligence that is able to process large amounts of data with high accuracy. DNN utilizes many layers of neurons to learn from complex data patterns. By implementing DNN in a chatbot, the system can understand the nuances of user questions and provide more accurate answers. DNN also allows chatbots to conduct sustainable learning, so that along with the increase in data, its performance can increase.

The Application of TF-IdF and DNN Methods

In this study, the TF-IdF and DNN methods were used to develop a chatbot that can provide accurate answers to user questions. The results obtained from this study showed an accuracy rate of 88.67%, indicating that the chatbot system that was built using the TF-IdF and DNN methods has a pretty good performance. The chatbot can process user questions, identify relevant keywords, and provide accurate answers. This is a promising first step for further development in the academic information system in the educational environment.

Analysis of the Application of the TF-IdF Method

The TF-IdF method is a crucial component of the chatbot system. It helps in identifying relevant keywords of questions raised by students. By analyzing the frequency of the appearance of words and its distribution in existing documents, chatbots can find out which information is more significant. This is very useful for building relevant knowledge bases and helping chatbots provide the right answer.

Excellence of the DNN Method

The DNN method is another crucial component of the chatbot system. It allows the system to understand the nuances of user questions and provide more accurate answers. DNN also allows chatbots to conduct sustainable learning, so that along with the increase in data, its performance can increase.

Conclusion

The application of the TF-IDF and DNN method in the development of academic information service chat at the USU Fasilkom-TI shows great potential in meeting the needs of student information. This chatbot is not only able to provide accurate answers, but also functions as a source of structured and accessible information at any time. With an accuracy of 88.67%, this research is a promising first step for further development in the academic information system in the educational environment. Thus, the application of this technology can improve the quality of information services and support students in their learning process.

Future Directions

The development of the chatbot system using the TF-IdF and DNN methods is a promising first step for further development in the academic information system in the educational environment. Future research can focus on improving the accuracy of the chatbot system, expanding the scope of the chatbot's knowledge base, and integrating the chatbot with other campus systems. Additionally, the chatbot can be used as a platform for students to interact with each other, share knowledge, and collaborate on projects.

Limitations of the Study

This study has several limitations. Firstly, the chatbot system was developed using a limited dataset, which may not be representative of the entire student population. Secondly, the accuracy of the chatbot system may be affected by the quality of the data used to train the model. Finally, the chatbot system may not be able to handle complex or ambiguous questions.

Recommendations for Future Research

Future research can focus on addressing the limitations of this study. Firstly, a larger and more diverse dataset can be used to train the chatbot model. Secondly, the chatbot system can be tested with a larger and more diverse group of students to evaluate its accuracy and effectiveness. Finally, the chatbot system can be integrated with other campus systems to provide a more comprehensive and seamless experience for students.

Conclusion

In conclusion, the application of the TF-IDF and DNN method in the development of academic information service chat at the USU Fasilkom-TI shows great potential in meeting the needs of student information. This chatbot is not only able to provide accurate answers, but also functions as a source of structured and accessible information at any time. With an accuracy of 88.67%, this research is a promising first step for further development in the academic information system in the educational environment.
Q&A: Application of the TF-Idf and Deep Neural Network (DNN) Methods in the USU Fasilkom-TI Academic Information Service Chatbot

Q: What is the purpose of the chatbot system developed in this study?

A: The purpose of the chatbot system developed in this study is to provide a platform for students to access information and ask questions related to academic matters. The chatbot system is designed to meet the information needs of students at the USU Fasilkom-TI.

Q: What are the TF-IdF and DNN methods, and how are they used in the chatbot system?

A: The TF-IdF method is a technique used to determine the importance of a word in the context of a particular document. The DNN method is a type of artificial intelligence that is able to process large amounts of data with high accuracy. In the chatbot system, the TF-IdF method is used to identify relevant keywords of questions raised by students, while the DNN method is used to process user questions and provide accurate answers.

Q: What are the benefits of using the TF-IdF and DNN methods in the chatbot system?

A: The benefits of using the TF-IdF and DNN methods in the chatbot system include improved accuracy of answers, ability to process complex and ambiguous questions, and sustainable learning capabilities. The chatbot system can also provide a centralized hub for students to access information and ask questions.

Q: What are the limitations of the chatbot system developed in this study?

A: The limitations of the chatbot system developed in this study include the use of a limited dataset, which may not be representative of the entire student population. Additionally, the accuracy of the chatbot system may be affected by the quality of the data used to train the model.

Q: How can the chatbot system be improved in the future?

A: The chatbot system can be improved in the future by using a larger and more diverse dataset to train the model, testing the chatbot system with a larger and more diverse group of students, and integrating the chatbot system with other campus systems.

Q: What are the potential applications of the chatbot system in the educational environment?

A: The potential applications of the chatbot system in the educational environment include providing a platform for students to access information and ask questions related to academic matters, supporting students in their learning process, and improving the quality of information services.

Q: How can the chatbot system be used to support students in their learning process?

A: The chatbot system can be used to support students in their learning process by providing a platform for students to ask questions and receive answers related to academic matters, offering guidance on academic matters, and providing support services.

Q: What are the potential benefits of using the chatbot system in the educational environment?

A: The potential benefits of using the chatbot system in the educational environment include improved student engagement, increased access to information, and improved academic outcomes.

Q: How can the chatbot system be integrated with other campus systems?

A: The chatbot system can be integrated with other campus systems by using APIs and other technologies to connect the chatbot system with other systems, such as student information systems and learning management systems.

Q: What are the potential challenges of implementing the chatbot system in the educational environment?

A: The potential challenges of implementing the chatbot system in the educational environment include ensuring that the chatbot system is user-friendly and accessible to all students, addressing concerns about the accuracy and reliability of the chatbot system, and ensuring that the chatbot system is integrated with other campus systems.

Q: How can the chatbot system be evaluated and assessed?

A: The chatbot system can be evaluated and assessed by using metrics such as accuracy, response time, and user satisfaction, as well as by conducting user testing and feedback sessions.

Q: What are the potential future directions for the chatbot system?

A: The potential future directions for the chatbot system include expanding the scope of the chatbot's knowledge base, improving the accuracy and reliability of the chatbot system, and integrating the chatbot system with other campus systems.