Chatbot Designing Information Service for New Student Registration Based on AIML and Machine Learning

Abstract

One of the efforts made by universities to serve prospective students is by providing consulting services and information that is usually carried out directly at the booth provided, through phone service or live chat support available on the college website. Increased visitors will result in waiting times due to limited availability of officers, which results in decreased satisfaction of prospective new students, moreover this service is only available during campus operating hours. One alternative solution to overcome this problem is to use Chatbot, able to answer questions raised by prospective new students which can be categorized as Frequently Asked Questions abbreviated as FAQ. Chatbot technology can be developed with a variety of AI (Artificial Intelligence) techniques. One of them is the AIML (Artificial Intelligence Markup Language) technique. One of the main drawbacks of AIML is that there is no reasoning ability so a learning system that is focused on supervised learning is needed. In the chatbot that will be built the learning process uses a selective neural conversational model or commonly called the Deep Semantic Similarity Model (DSSM) developed by Microsoft. Meanwhile, the measurement of chatbot performance will be done using Confusion Matrix which is a method of evaluating the performance of the algorithm from Machine Learning (ML). The results of the study stated that the chatbot system that was built was able to answer questions posed by prospective students properly and correctly while the questions were available in the chatbot knowledge base.

Intelligent Chatbot for Admission in Higher Education

“…Natural language processing is classified into two parts: (1) natural language understanding and (2) natural language generation. These two parts represent the task of understanding and generating the text [29].…”

“…Natural Language Understanding (NLU) as a science of language includes sentence structure, phonology (sound), morphology (word formation), syntax, semantics, and Pragmatics (understanding) [29]. Chatbots use NLU to derive context from unstructured input of the user in human language and respond based on the current user's purpose [30,31].…”

Public perceptions towards ChatGPT​ a​s the​ Robo​-Assistant

“…Chatbots use AI techniques to understand natural language, identify meaning and emotions, and design meaningful responses, for example, making it simple for customers to get timely answers to their questions without wasting time waiting on phone lines or sending emails repeatedly. Chatbots have the potential to reduce the number of customer calls, the average handling time, and the costs associated with customer service (Wijaya & Zoromi, 2020). Furthermore, robo-advisors have been applauded as the next operating system in finance and the new wealth management interface of the 21 st century.…”

English and Arabic Chatbots: A Systematic Literature Review

“…Automat ic based Metric F1-Score [89], [114], [106], [80], [62], [100] Precision [105], [106], [80], [62] Recall [105], [106], [80], [62] Accuracy [92], [114], [126], [105], [124], [80], [ 93], [62], [100], [76], [56], [74] PPL [89], [102], [94], [92], [98], [124], [12 6], [39], [101], [85] BLEU [79], [83], [94], [81], [92], [95], [84], [ 126], [127], [86], [101], [49], [40],[4 0],…”