CLG clustering for dropout prediction using log-data clustering method

Agung Triayudi, Wahyu Oktri Widyarto, Lia Kamelia, Iksal Iksal, Sumiati Sumiati


Implementation of data mining, machine learning, and statistical data from educational department commonly known as Educational Data Mining (EDM). Most of school systems require a teacher to teach a number of students at one time. Exam are regularly being use as a method to measure student’s achievement, which is difficult to understand because examination can’t be done easily. The other hand, programming classes makes source code editing and UNIX commands able to easily detected and stored automatically as log-data. Hence, rather that estimating the performance of those student based on this log-data, this study being more focused on detecting them who experienced a difficulties or unable to take programming classes. We propose CLG Clustering methods that can predict a risk of being dropped out from school using cluster data for outlier detection.


Dropout prediction, EDM, k-means, Outlier detection, UNIX commands



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