Open AccessCLG clustering for dropout prediction using log-data clustering method
Author(s) -
Agung Triayudi,
Wahyu Oktri Widyarto,
Lia Kamelia,
Iksal Iksal,
Sumiati Sumiati
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i3.pp764-770
Subject(s) - computer science , cluster analysis , unix , dropout (neural networks) , source code , outlier , code (set theory) , data mining , machine learning , artificial intelligence , programming language , software , set (abstract data type)
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.