PEMANFAATAN NATURAL LANGUAGE PROCESSING DAN PATTERN MATCHING DALAM PEMBELAJARAN MELALUI GURU VIRTUAL

Authors

  • Migunani
  • Kevin Aditama Universitas STEKOM

DOI:

https://doi.org/10.51903/elkom.v13i1.187

Keywords:

Virtual Teacher, Learning, Natural Language Processing, Pattern Matching.

Abstract

Virtual teachers are devices or agents with the function of replacing the role of humans (teachers) with their teaching skills. The virtual teacher also has a function as task facilitator and is presented during training, especially when the teacher is actually not present. The virtual teacher's objectivity is carried out by explaining the subject matter in a virtual environment, for example, virtual independent learning. In the western world, virtual schools are quite widely applied, especially to students who are homeschooling. In a virtual school, all materials, assignments and grades are accessed via the internet. The virtual teacher concept refers to a device that provides guidance and practice. Teachers who are equipped with interactive agent functions that can provide direction and feedback in order to improve student abilities. By utilizing natural language processing (NLP) and pattern matching to help students learn alone without being accompanied by a teacher. NLP is used as a learning tool as outlined in the text of the sentence as part of the learning material which is done virtually. Meanwhile, pattern matching is used to group the questions asked so that the computer can gain an understanding of the questions posed by students. This grouping aims to solve problems with different word order which basically have different meanings. The teacher has the facility to make material, make questions, and answer student questions. A teacher can add knowledge about the database to enrich the source of knowledge for students.

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Downloads

Published

2020-08-19

How to Cite

[1]
Migunani and Kevin Aditama, “PEMANFAATAN NATURAL LANGUAGE PROCESSING DAN PATTERN MATCHING DALAM PEMBELAJARAN MELALUI GURU VIRTUAL”, ELKOM, vol. 13, no. 1, pp. 121–133, Aug. 2020.