A new smart learning framework using artificial intelligence
This research focused on developing a new smart framework to be applied to online learning. This smart framework aimed to enable users to obtain online learning materials that fit with the user's abilities. User capabilities were classified using artificial intelligence (AI). This user ability...
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Online Access: | http://eprints.utm.my/id/eprint/92462/ http://dx.doi.org/10.1109/ICOVET50258.2020.9230323 |
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my.utm.924622021-09-30T15:11:45Z http://eprints.utm.my/id/eprint/92462/ A new smart learning framework using artificial intelligence Wahyono, Irawan Dwi Asfani, Khoirudin Mohamad, Mohd. Murtadha Ashar, M. Arif Nur Afandi, Arif Nur Afandi Aripriharta, Aripriharta QA75 Electronic computers. Computer science This research focused on developing a new smart framework to be applied to online learning. This smart framework aimed to enable users to obtain online learning materials that fit with the user's abilities. User capabilities were classified using artificial intelligence (AI). This user ability classification aimed to get material that matched the user's ability. The AI algorithm used in this framework was K-Nearest Neighbor (K-NN). K-NN had the duty to process the pre-test result data and the results were matched with various attribute data owned by the user. In this study, a user ability classification test and overall system accuracy testing were based on the post-test results obtained after the user completed the learning he had done. The overall results of system testing obtained in this framework shown an average accuracy rate of 90% with an error of 10%. 2020 Conference or Workshop Item PeerReviewed Wahyono, Irawan Dwi and Asfani, Khoirudin and Mohamad, Mohd. Murtadha and Ashar, M. and Arif Nur Afandi, Arif Nur Afandi and Aripriharta, Aripriharta (2020) A new smart learning framework using artificial intelligence. In: 2020 4th International Conference on Vocational Education and Training (ICOVET), 19 - 19 September 2020, Malang, Indonesia. http://dx.doi.org/10.1109/ICOVET50258.2020.9230323 |
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QA75 Electronic computers. Computer science Wahyono, Irawan Dwi Asfani, Khoirudin Mohamad, Mohd. Murtadha Ashar, M. Arif Nur Afandi, Arif Nur Afandi Aripriharta, Aripriharta A new smart learning framework using artificial intelligence |
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This research focused on developing a new smart framework to be applied to online learning. This smart framework aimed to enable users to obtain online learning materials that fit with the user's abilities. User capabilities were classified using artificial intelligence (AI). This user ability classification aimed to get material that matched the user's ability. The AI algorithm used in this framework was K-Nearest Neighbor (K-NN). K-NN had the duty to process the pre-test result data and the results were matched with various attribute data owned by the user. In this study, a user ability classification test and overall system accuracy testing were based on the post-test results obtained after the user completed the learning he had done. The overall results of system testing obtained in this framework shown an average accuracy rate of 90% with an error of 10%. |
format |
Conference or Workshop Item |
author |
Wahyono, Irawan Dwi Asfani, Khoirudin Mohamad, Mohd. Murtadha Ashar, M. Arif Nur Afandi, Arif Nur Afandi Aripriharta, Aripriharta |
author_facet |
Wahyono, Irawan Dwi Asfani, Khoirudin Mohamad, Mohd. Murtadha Ashar, M. Arif Nur Afandi, Arif Nur Afandi Aripriharta, Aripriharta |
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Wahyono, Irawan Dwi |
title |
A new smart learning framework using artificial intelligence |
title_short |
A new smart learning framework using artificial intelligence |
title_full |
A new smart learning framework using artificial intelligence |
title_fullStr |
A new smart learning framework using artificial intelligence |
title_full_unstemmed |
A new smart learning framework using artificial intelligence |
title_sort |
new smart learning framework using artificial intelligence |
publishDate |
2020 |
url |
http://eprints.utm.my/id/eprint/92462/ http://dx.doi.org/10.1109/ICOVET50258.2020.9230323 |
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