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CLG clustering for dropout prediction using log-data clustering method

IAES International Journal of Artificial Intelligence · Vol. 10 No. 3 (2021) · pp. 764–770

DOI
10.11591/ijai.v10.i3.pp764-770
ISSN
2252-8938, 2089-4872
Published
2021-09-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

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.

Keywords

Collaboration network of the authors

40 authors · 85 co-authorship links · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Bandung Institute of Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Rusli MalliAffiliation unknown3
Nanang IsmailAffiliation unknown3
Adam FaroqiAffiliation unknown3
Mawardi PewangiAffiliation unknown2
Akmaliyah AkmaliyahAffiliation unknown2
Rina MardiatiBandung Institute of Technology2
Yadi JanwariAffiliation unknown2
Putri Diesy FitrianiAffiliation unknown2
Y. RetnaniAffiliation unknown2
Ma’ruf KasimUniversitas Halu Oleo1
Halili HaliliUniversitas Halu Oleo1
Liza Sasmita SariAffiliation unknown1
Rizki Rahma PutriAffiliation unknown1
Mumtahanah MumtahanahAffiliation unknown1
Nuril HudaAffiliation unknown1
Nurhidaya MAffiliation unknown1
dina herminaAffiliation unknown1
Wahidah OctavianiAffiliation unknown1
Abdul Azis MusliminAffiliation unknown1
Husna AkbarAffiliation unknown1
Nur Fadli TufailAffiliation unknown1
Mursalin MursalinAffiliation unknown1
Abbas Baco MiroAffiliation unknown1
Nurmalia NurmaliaAffiliation unknown1
Ija SrirahmawatiAffiliation unknown1
Ilham IlhamAffiliation unknown1
Liberty ChaidirAffiliation unknown1
Tb. Ai MunandarAffiliation unknown1
Arifina FebriasariAffiliation unknown1
Suryaman SuryamanAffiliation unknown1
Sulasno SulasnoAffiliation unknown1
Apriliana DwijayantiAffiliation unknown1
Deni Kamaludin YusupAffiliation unknown1
Tatang AstarudinAffiliation unknown1
Khairunnisa NurrahmahAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">CLG clustering for dropout prediction using log-data clustering method</dc:title>
	<dc:creator>Triayudi, Agung</dc:creator>
	<dc:creator>Widyarto, Wahyu Oktri</dc:creator>
	<dc:creator>Kamelia, Lia</dc:creator>
	<dc:creator>Iksal, Iksal</dc:creator>
	<dc:creator>Sumiati, Sumiati</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Dropout prediction;  Educational data mining; k-means; Outlier detection; UNIX commands</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20927</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i3.pp764-770</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 3: September 2021; 764-770</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20927/13228</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>