CLASSIFICATION OF STUDENT SATISFACTION WITH ONLINE LECTURE

Nanang Ruhyana; Tati Mardiana; Fachri Amsury; Daning Nur Sulistyowati

doi:10.34288/jri.v4i1.144

Authors

Nanang Ruhyana Universitas Nusa Mandiri
Tati Mardiana Universitas Nusa Mandiri
Fachri Amsury Universitas Nusa Mandiri
Daning Nur Sulistyowati Universitas Nusa Mandiri

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v4i1.144

Keywords:

covid 19, data mining, online, k-NN, decision tree

Abstract

Abstra Covid-19 has had a significant impact on people's lives, resulting in the paralysis of almost the entire economy and education, especially in the education sector, resulting in many students being unable to carry out teaching and learning activities at schools or universities. Based on this, the Ministry of Education and Culture has issued an appeal to stop face-to-face teaching and learning activities at schools and universities and replace them with distance or online learning. Resulting in teaching and learning activities to be less than optimal for students or students, there is dissatisfaction with the distance or online learning system, the purpose of this study is to measure the level of student satisfaction with online lectures by applying data mining techniques, classifying the level of online learning satisfaction using an online learning approach. k-NN algorithm and Decision Tree with 100 questionnaire data that has been collected from active students who carry out online lectures with an accuracy rate of 96.00% from the k-NN algorithm and a satisfied precision value of 95.51%, a satisfied recall value of 98.84% on a precision value the dissatisfied class is 90.91%, the recall value of the dissatisfied class is 71.43%. While the accuracy results using the Decision Tree algorithm approach is lower with an accuracy of 95.00%. based on research results that the level of student satisfaction with distance learning or online is quite high.

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