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Indriyanti
Bina Sarana Informatika University
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Grouping of Success Levels in E-Learning Learning Factors: Approaches with Machine Learning Algorithm Elin Panca Saputra; Sugiono; Indriyanti; Supriatiningsih; Hafis Nurdin
Jurnal Mantik Vol. 5 No. 1 (2021): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol5.2021.1271.pp78-85

Abstract

The purpose of this study is to obtain the results of the modeling process on grouping the results of student learning, and to produce student success rates, while to find the results of the accuracy level of student learning success based on E-Learning with the Support Vectore Machine (SVM) method. In this grouping, there are 5 clusters. Furthermore, the process of counting can be as many as 2 iterations, namely getting the final result in the form of Cluster-1 with a total of 10 students, cluster-2 with a total of 45 students, cluster-3 with a total of 22 students, cluster 4 with a total of 13 students, and the next is cluster-5 with a total of 19 students. Then the results of the resulting process with a total of 5 types of clusters, namely from students who get the highest results to the lowest. In addition, this study also looks for the level of accuracy in e-learning student learning processes using the Support Vectore Machine (SVM) method, the accuracy results obtained are 90.91%, while the AUC results are 82.81%. then the value of the calculated accuracy rate can be classified as accuracy with the predicate result that is good.
Co-Authors Hafis Nurdin Panca Saputra, Elin Sugiono Supriatiningsih