UNDIKSHA VIRTUAL ASSISTANT (SHAVIRA): INTEGRATION FREQUENCY ASKED QUESTION WITH RASA FRAMEWORK - Resika Arthana, I Ketut; Dewi, Luh Joni Erawati; Seputra, Ketut Agus; Marti, Ni Wayan (JST (Jurnal Sains dan Teknologi), 2021)
Nowadays, the implementation of chatbots in information systems is getting popular to increase user experience. In our previous research, we developed Undiksha Virtual Assistant (Shavira). Shavira is a knowledge management system (KMS) as Frequency Asked Question (FAQ) system to facilitate users to get an answer about their questions. The challenge on the existing Shavira system is that the users must open a website and type queries to get a list of answers. The text query must match with the keyword in systems. To address this problem and to avoid cognitive load, in this research we implemented a chatbot in Shavira. The purpose of the chatbot implementation in Shavira is to make information easier to be accessed and to increase user experience. The users can ask a question in natural language and get an answer immediately on their chat application such as: Telegram, Facebook, or Messenger. We used Rasa Framework as a chatbot engine in Shavira. Rasa Framework is an open-source virtual assistant engine based on artificial intelligence. The challenge in conducting this research is in integrating the existing FAQ system with Rasa Framework. This research consists of four phases, they are: adaptation data structure in the existing FAQ with data structure requested by Rasa framework, data integration and mapper, and evaluation. The result of this research is a new Shavira that is integrated with a chatbot and can be accessed from chat applications such as Telegram.Â  We evaluated the accuracy of Shavira powered by Rasa Framework with ten topics about academic information. The results showed that the accuracy of Shavira is 90%. We also evaluated the usability of Shavira with Software Usability Scale (SUS) Questionnaire. The usability evaluation showed that Shavira satisfied users with valueÂ  81 (threshold 68) and categorized Excellent, Acceptable and Prometer.