Authors:
Mesra Yel
1
;
Syahril Efendi
2
;
Muhammad Zarlis
3
and
Saib Suwilo
2
Affiliations:
1
Graduate Program of Computer Science, Department of Computer Science, Universitas Sumatera Utara, Medan, Indonesia
;
2
Department of Computer Science, Universitas Sumatera Utara, Medan, Indonesia
;
3
Information System Management Department, BINUS Graduate Program – Master of Information System Management, Bina Nusantara University, Jakarta, Indonesia
Keyword(s):
Differentiated E-Learning, Diagnostic Assessment Classification, K-Means Clustering Diagnostic Assessment.
Abstract:
E-Learning is a way of teaching and learning online in virtual classrooms which gives experiences, changes and needs according to technological developments and new learning paradigms with the flexibility given to educators in formulating learning designs and assessments. This research contributes to producing a student classification model using the k-means clustering algorithm to be applied to differentiated e-learning, which can accommodate all user needs according to abilities, interests, and talents by using artificial intelligence. The result is a differentiated e-learning model is produced to classify diagnostic assessments. Based on test data on 20 students, it was successfully classified into 3 clusters, namely students in class 1 with a trend of X values being in the do not know the value, Y with a very ignorant value while the Y value is in a value between not knowing and partially understanding with a total of 9 people. Students are in grade 3 with a trend with a value of
X being between not knowing and partial understanding, Y with a partially understanding value, while the Y value is at a very ignorant value with a total of 6 people with an accuracy level of f1 test score 100%.
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