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Problem statement: Artificial intelligence chatbot is a technology that makes interactions&#13; between men and machines using natural language possible. From literature of chatbots&#13; keywords/pattern matching techniques, potential issues for improvement had been discovered. The&#13; discovered issues are in the context of keywords arrangement for matching precedence and keywords&#13; variety for matching flexibility. Approach: Combining previous techniques/mechanisms with some&#13; additional adjustment, new technique to be used for keywords matching process is proposed. Using&#13; newly developed chatbot named ViDi (abbreviation for Virtual Diabetes physician which is a chatbot&#13; for diabetes education activity) as a testing medium, the proposed technique named One-Match and&#13; All-Match Categories (OMAMC) is being used to test the creation of possible keywords surrounding&#13; one sample input sentence. The result for possible keywords created by this technique then being&#13; compared to possible keywords created by previous chatbots techniques surrounding the same sample&#13; sentence in matching precedence and matching flexibility context. Results: OMAMC technique is&#13; found to be improving previous matching techniques in matching precedence and flexibility context.&#13; This improvement is seen to be useful for shortening matching time and widening matching flexibility&#13; within the chatbots keywords matching process. Conclusion: OMAMC for keywords matching in&#13; chatbot is shown to be an improvement over previous techniques in the context of keywords&#13; arrangement for matching precedence and keywords variety for matching flexibility

One-Match and All-Match Categories for Keywords Matching in Chatbot