Classification Technique of Interviewer-Bot Result using Naïve Bayes and Phrase Reinforcement Algorithms

Moechammad Sarosa, Mochammad Junus, Mariana Ulfah Hoesny, Zamah Sari, Martin Fatnuriyah


Students with hectic college schedules tend not to have enough time repeating the course material. Meanwhile, after they graduated, to be accepted in a foreign company with a higher salary, they must be ready for the English-based interview. To meet these needs, they try to practice conversing with someone who is proficient in English. On the other hand, it is not easy to have someone who is not only proficient in English, but also understand about a job interview related topics. This paper presents the development of a machine which is able to provide practice on English-based interviews, specifically on job interviews. Interviewer machine (interviewer bot) is expected to help students practice on speaking English in particular issue of finding suitable job. The interviewer machine design uses words from a chat bot database named ALICE to mimic human intelligence that can be applied to a search engine using AIML. Naïve Bayes algorithm is used to classify the interview results into three categories: POTENTIAL, TALENT and INTEREST students. Furthermore, based on the classification result, the summary is made at the end of the interview session by using phrase reinforcement algorithms. By using this bot, students are expected to practice their listening and speaking skills, also to be familiar with the questions often asked in job interviews so that they can prepare the proper answers. In addition, the bot’ users could know their potential, talent and interest in finding a job, so they could apply to the appropriate companies. Based on the validation results of 50 respondents, the accuracy degree of interviewer chat-bot (interviewer engine) response obtained 86.93%.


Job interview, Words classification, Natural language processing, Machine

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Copyright (c) 2018 Moechammad Sarosa

International Journal of Emerging Technologies in Learning. ISSN: 1863-0383
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