Shared nearest neighbour in text mining for classification material in online learning using mobile application
There are many resources for media learning in online learning that all of the teachers made many media which it made a problem if there have the same subject and material. This problem made online learning having a big database and many materials made useless because the material has the same purpo...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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International Association of Online Engineering
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/98679/1/MohdMurtadhaMohamad2022_SharedNearestNeighbourinTextMining.pdf |
| _version_ | 1796866668096389120 |
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| author | Wahyono, Irawan Dwi Saryono, Djoko Putranto, Hari Asfani, Khoirudin Rosyid, Harits Ar Sunarti, Sunarti Mohamad, Mohd. Murtadha Mohamad Said, Mohd. Nihra Haruzuan Gwo, Jiun Horng Jia, Shing Shih |
| author_facet | Wahyono, Irawan Dwi Saryono, Djoko Putranto, Hari Asfani, Khoirudin Rosyid, Harits Ar Sunarti, Sunarti Mohamad, Mohd. Murtadha Mohamad Said, Mohd. Nihra Haruzuan Gwo, Jiun Horng Jia, Shing Shih |
| author_sort | Wahyono, Irawan Dwi |
| collection | ePrints |
| description | There are many resources for media learning in online learning that all of the teachers made many media which it made a problem if there have the same subject and material. This problem made online learning having a big database and many materials made useless because the material has the same purpose. The big problem in overload database is that online learning can’t be accessed by everyone. This research to fix this problem developed an algorithm in Artificial Intelligence for the classification of material in online learning with the same subject and purpose so that teachers can use already media. This algorithm is text mining and Shared Nearest Neighbour (SSN) that is embedded in the mobile application to display the classification and the location of searching media in database online learning. The testing in this research applied in 142 media with 130 data training and 12 data testing is the result of testing is 94.7% of the accuracy of the algorithm and The average of validation is 73.33%. |
| first_indexed | 2024-03-05T21:15:36Z |
| format | Article |
| id | utm.eprints-98679 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:15:36Z |
| publishDate | 2022 |
| publisher | International Association of Online Engineering |
| record_format | dspace |
| spelling | utm.eprints-986792023-01-30T04:53:22Z http://eprints.utm.my/98679/ Shared nearest neighbour in text mining for classification material in online learning using mobile application Wahyono, Irawan Dwi Saryono, Djoko Putranto, Hari Asfani, Khoirudin Rosyid, Harits Ar Sunarti, Sunarti Mohamad, Mohd. Murtadha Mohamad Said, Mohd. Nihra Haruzuan Gwo, Jiun Horng Jia, Shing Shih QA75 Electronic computers. Computer science There are many resources for media learning in online learning that all of the teachers made many media which it made a problem if there have the same subject and material. This problem made online learning having a big database and many materials made useless because the material has the same purpose. The big problem in overload database is that online learning can’t be accessed by everyone. This research to fix this problem developed an algorithm in Artificial Intelligence for the classification of material in online learning with the same subject and purpose so that teachers can use already media. This algorithm is text mining and Shared Nearest Neighbour (SSN) that is embedded in the mobile application to display the classification and the location of searching media in database online learning. The testing in this research applied in 142 media with 130 data training and 12 data testing is the result of testing is 94.7% of the accuracy of the algorithm and The average of validation is 73.33%. International Association of Online Engineering 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/98679/1/MohdMurtadhaMohamad2022_SharedNearestNeighbourinTextMining.pdf Wahyono, Irawan Dwi and Saryono, Djoko and Putranto, Hari and Asfani, Khoirudin and Rosyid, Harits Ar and Sunarti, Sunarti and Mohamad, Mohd. Murtadha and Mohamad Said, Mohd. Nihra Haruzuan and Gwo, Jiun Horng and Jia, Shing Shih (2022) Shared nearest neighbour in text mining for classification material in online learning using mobile application. International Journal of Interactive Mobile Technologies, 16 (4). pp. 159-168. ISSN 1865-7923 http://dx.doi.org/10.3991/ijim.v16i04.28991 DOI: 10.3991/ijim.v16i04.28991 |
| spellingShingle | QA75 Electronic computers. Computer science Wahyono, Irawan Dwi Saryono, Djoko Putranto, Hari Asfani, Khoirudin Rosyid, Harits Ar Sunarti, Sunarti Mohamad, Mohd. Murtadha Mohamad Said, Mohd. Nihra Haruzuan Gwo, Jiun Horng Jia, Shing Shih Shared nearest neighbour in text mining for classification material in online learning using mobile application |
| title | Shared nearest neighbour in text mining for classification material in online learning using mobile application |
| title_full | Shared nearest neighbour in text mining for classification material in online learning using mobile application |
| title_fullStr | Shared nearest neighbour in text mining for classification material in online learning using mobile application |
| title_full_unstemmed | Shared nearest neighbour in text mining for classification material in online learning using mobile application |
| title_short | Shared nearest neighbour in text mining for classification material in online learning using mobile application |
| title_sort | shared nearest neighbour in text mining for classification material in online learning using mobile application |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/98679/1/MohdMurtadhaMohamad2022_SharedNearestNeighbourinTextMining.pdf |
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