CLG clustering for dropout prediction using log-data clustering method

Agung Triayudi (Universitas Nasional)
Wahyu Oktri Widyarto (Universitas Serang Raya)
Lia Kamelia (UIN Sunan Gunung Djati)
Iksal Iksal (Universitas Faletehan)
Sumiati Sumiati (Universitas Serang Raya)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 10, No 3: September 2021

Implementation of data mining, machine learning, and statistical data from educational department commonly known as educational data mining. 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 cannot be done easily. The other hand, programming classes makes source code editing and UNIX commands able to easily detect and store 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 difficulty 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.