Educational Data Mining Using Cluster Analysis Methods and Decision
Trees based on Log Mining

Educational Data Mining Using Cluster Analysis Methods and Decision
Trees based on Log Mining

Analysis Cluster, Decision Tree, Educational Data Mining (EDM), Log, SPADA

Higher education institutions store data keep growing every year. The data has important information, but it still not optimized
into knowledge. Data Mining (DM) can be used to process existing data in universities in order to obtain knowledge that can
be utilized further. Educational Data Mining (EDM) often appears to be applied in big data processing in the education sector.
One of the educational data that can be further processed with EDM is activity log data from an e-learning system used in
teaching and learning activities. The log activity can be further processed more specifically by using log mining. The purpose
of this study was to process log data from the Sebelas Maret University Online Learning System (SPADA UNS) to determine
student learning behavior patterns and their relationship to the final results obtained. The data mining method applied in this
research is cluster analysis with the K-means Clustering and Decision Tree algorithms. The clustering process is used to find
groups of students who have similar learning patterns. While the decision tree is used to model the results of the clustering in
order to enable the analysis and decision-making processes. Processing of 11,139 SPADA UNS log data resulted in 3 clusters
with a Davies Bouldin Index (DBI) value of 0.229. The results of these three clusters are modeled by using a Decision Tree.
The decision tree model in cluster 0 represents a group of students who have a low tendency of learning behavior patterns with
the highest frequency of access to course viewing activities obtained accuracy of 74.42% . In cluster 1, which contains groups
of students with high learning behavior patterns, have a high frequency of access to viewing discussion activities obtained
accuracy of 76.47%. While cluster 2 is a group of students who have a pattern of learning behavior that is having a high
frequency of access to the activity of sending assignments obtained accuracy of 90.00%

Safira Nury Safitri1
, Haryono Setiadi2
, Esti Suryani3

Universitas Sebelas Maret

30-06-2022

Fajar bagus W

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Indonesia

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