Emotion detection based on column comments in material of online learning using 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 unhap...
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Kassel University Press GmbH
2022
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Online Access: | http://eprints.utm.my/id/eprint/98469/1/MohdNihraHaruzuan2022_EmotionDetectionbasedonColumnComments.pdf http://eprints.utm.my/id/eprint/98469/ http://dx.doi.org/10.3991/IJIM.V16I03.28963 |
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my.utm.984692023-01-08T02:53:19Z http://eprints.utm.my/id/eprint/98469/ Emotion detection based on column comments in material of online learning using artificial intelligence Dwi Wahyono, Irawan Saryono, Djoko Putranto, Hari Asfani, Khoirudin Ar Rosyid, Harits Sunarti, Sunarti Mohamad, Mohd. Murtadha Mohamad Said, Mohd. Nihra Haruzuan Horng, Gwo Jiun Shih, Jia Shing L Education (General) 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. Kassel University Press GmbH 2022-02 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/98469/1/MohdNihraHaruzuan2022_EmotionDetectionbasedonColumnComments.pdf Dwi Wahyono, Irawan and Saryono, Djoko and Putranto, Hari and Asfani, Khoirudin and Ar Rosyid, Harits and Sunarti, Sunarti and Mohamad, Mohd. Murtadha and Mohamad Said, Mohd. Nihra Haruzuan and Horng, Gwo Jiun and Shih, Jia Shing (2022) Emotion detection based on column comments in material of online learning using artificial intelligence. International Journal of Interactive Mobile Technologies, 16 (3). pp. 82-91. ISSN 1865-7923 http://dx.doi.org/10.3991/IJIM.V16I03.28963 DOI:10.3991/IJIM.V16I03.28963 |
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L Education (General) Dwi Wahyono, Irawan Saryono, Djoko Putranto, Hari Asfani, Khoirudin Ar Rosyid, Harits Sunarti, Sunarti Mohamad, Mohd. Murtadha Mohamad Said, Mohd. Nihra Haruzuan Horng, Gwo Jiun Shih, Jia Shing Emotion detection based on column comments in material of online learning using artificial intelligence |
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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. |
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Article |
author |
Dwi Wahyono, Irawan Saryono, Djoko Putranto, Hari Asfani, Khoirudin Ar Rosyid, Harits Sunarti, Sunarti Mohamad, Mohd. Murtadha Mohamad Said, Mohd. Nihra Haruzuan Horng, Gwo Jiun Shih, Jia Shing |
author_facet |
Dwi Wahyono, Irawan Saryono, Djoko Putranto, Hari Asfani, Khoirudin Ar Rosyid, Harits Sunarti, Sunarti Mohamad, Mohd. Murtadha Mohamad Said, Mohd. Nihra Haruzuan Horng, Gwo Jiun Shih, Jia Shing |
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Dwi Wahyono, Irawan |
title |
Emotion detection based on column comments in material of online learning using artificial intelligence |
title_short |
Emotion detection based on column comments in material of online learning using artificial intelligence |
title_full |
Emotion detection based on column comments in material of online learning using artificial intelligence |
title_fullStr |
Emotion detection based on column comments in material of online learning using artificial intelligence |
title_full_unstemmed |
Emotion detection based on column comments in material of online learning using artificial intelligence |
title_sort |
emotion detection based on column comments in material of online learning using artificial intelligence |
publisher |
Kassel University Press GmbH |
publishDate |
2022 |
url |
http://eprints.utm.my/id/eprint/98469/1/MohdNihraHaruzuan2022_EmotionDetectionbasedonColumnComments.pdf http://eprints.utm.my/id/eprint/98469/ http://dx.doi.org/10.3991/IJIM.V16I03.28963 |
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1755872313648087040 |
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13.18916 |