Shared nearest neighbour in text mining for classification material in online learning using mobile application

• Main Authors: 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
• Format: Article
• Language: English
• Published: International Association of Online Engineering 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/98679/1/MohdMurtadhaMohamad2022_SharedNearestNeighbourinTextMining.pdf; http://eprints.utm.my/id/eprint/98679/; http://dx.doi.org/10.3991/ijim.v16i04.28991
• Institution: Universiti Teknologi Malaysia

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%.