Peningkatan Kinerja Chatbot NLP Asisten: Tinjauan Literatur tentang Metode dan Akurasi dalam Aplikasi Berbasis Percakapan

Abstract

Chatbots have been widely used in various industries such as e-commerce, banking, healthcare, and education to improve efficiency and provide 24/7 services to users. In the field of education, NLP chatbot brings the potential to improve soft skills and hard skills through online learning. This research aims to find suitable methods from previous research to be used in the creation of a conversational chatbot for supporting services of an application system. The research method used is Systematic Literature Review, with comprehensive journal search steps using appropriate keyword search strategies. The research results include 20 articles relevant to the topic of chatbot NLP assistants. The various methods identified in the research include machine learning, deep learning, rule-based approaches, and the use of third-party applications such as Dialogflow and IBM Watson. The analysis results show that the Dynamic Memory Network (DMN) method has the best performance with 91% accuracy. DMN combines the advantages of LSTM and Memory Network with a dynamic attention mechanism, allowing the model to focus on the most relevant information in sequential data. Although this study provides interesting findings, further research is needed to deal with the different characteristics and availability of data in various real-world scenarios. Thus, this article highlights the importance of continuously developing NLP chatbot technology for better applications and improved service quality for users. It is hoped that this article can contribute to the development of research related to NLP chatbot assistants in better and more efficient application systems.