TY - GEN
T1 - Classifying the Students' Behavior on e-Learning System using Fine-Tuning K-NN Method
AU - Yamasari, Yuni
AU - Qoiriah, Anita
AU - Rochmawati, Naim
AU - Suartana, I. M.
AU - Putra, Oddy Virgantara
AU - Ahmad, Tohari
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - monitoring the student's behavior is challenging for teachers in online learning, which is crucial to solving. It is because, in this pandemic period, online learning is required to minimize the spreading of coronavirus. However, research in this domain is not much. This study provides an alternative to this problem by classifying students' behavior in the e-Learning system, where the k-NN is applied to mine the students' behavior. In addition, this paper also tests the proper parameters to improve the performance of k-NN: k and distance. The experimental result shows that the best performance on the cross-validation technique is reached by Euclidean distance and, on the percentage-split, is achieved by distance-Manhattan. These are indicated by the highest accuracy level obtained by neighbors of five and 20 fold, about 96.9% on the cross-validation technique. On the percentage split technique, the highest accuracy level, about 95.3%, is reached by neighbors of four and split 50%. In this best performance, four students are misclassified on the cross-validation and six on the percentage split.
AB - monitoring the student's behavior is challenging for teachers in online learning, which is crucial to solving. It is because, in this pandemic period, online learning is required to minimize the spreading of coronavirus. However, research in this domain is not much. This study provides an alternative to this problem by classifying students' behavior in the e-Learning system, where the k-NN is applied to mine the students' behavior. In addition, this paper also tests the proper parameters to improve the performance of k-NN: k and distance. The experimental result shows that the best performance on the cross-validation technique is reached by Euclidean distance and, on the percentage-split, is achieved by distance-Manhattan. These are indicated by the highest accuracy level obtained by neighbors of five and 20 fold, about 96.9% on the cross-validation technique. On the percentage split technique, the highest accuracy level, about 95.3%, is reached by neighbors of four and split 50%. In this best performance, four students are misclassified on the cross-validation and six on the percentage split.
KW - behavior
KW - classification
KW - e-Learning
KW - k-NN
KW - student
UR - http://www.scopus.com/inward/record.url?scp=85145345982&partnerID=8YFLogxK
U2 - 10.1109/ICOIACT55506.2022.9972117
DO - 10.1109/ICOIACT55506.2022.9972117
M3 - Conference contribution
AN - SCOPUS:85145345982
T3 - ICOIACT 2022 - 5th International Conference on Information and Communications Technology: A New Way to Make AI Useful for Everyone in the New Normal Era, Proceeding
SP - 82
EP - 86
BT - ICOIACT 2022 - 5th International Conference on Information and Communications Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information and Communications Technology, ICOIACT 2022
Y2 - 24 August 2022 through 25 August 2022
ER -