Emotion Detection based on Column Comments in Material of Online Learning using Artificial Intelligence

Emotion Detection based on Column Comments in Material of Online Learning using Artificial Intelligence

emotion detection

artificial intelligence

many universities use online learning as media learning that each material of media which includes videos, textual content, or audio may be given remarks from college students. The lecture desires to recognize approximately the feelings of college students which include happy, disappointed, or unhappy when they accessed the media and instructors get an assessment of pleasant from their media. This study constructed a utility cellular for the detection of emotion from column remarks in the media online. The mobile application makes use of synthetic intelligence to type textual content from remarks and to decide the emotion of college students. The mobile application on a cellular device. The set of rules with inside the utility is k-Nearest Neighbour for the textual content mining feature in this study. The information of trying out these studies is commenting on YouTube channels and online studying which include SIPEJAR The result of trying it out is that the common accuracy is 0,697, the value of recall is 0.5595, and the common precision is 0, 4421 and the accuracy for the utility of this mobile app is 70% for detection emotion-primarily based totally on a column of remark in the media online.

Wahyono, Irawan Dwi

Saryono, Djoko

Putranto , Hari

Asfani , Khoirudin

Rosyid , Harits Ar

Sunarti

Mohamad , Mohd Murtadha

Mohamad Said, Mohd Nihra Haruzuan Bin

Horng, Gwo Jiun

Shih , Jia-Shing

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 03 (2022); pp. 82-91

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-02-10

Copyright (c) 2022 Irawan, Prof., Hari, Khoirudin, Harits, Sunarti , Dr., Nihra, Gwo, Jia

https://creativecommons.org/licenses/by/4.0

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article