Clustering Student Competencies Using the K-Means Algorithm

  This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Ultimatics : Jurnal Teknik Informatika Ročník 17; číslo 1; s. 99 - 106
Hlavní autori: Andini, Ratih Friska Dwi, Liantoni, Febri, Budianto, Aris
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: 01.07.2025
ISSN:2085-4552, 2581-186X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract   This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas of competence as features. The K-Means algorithm is used to group student data into several clusters based on academic grade patterns. The results show that the K-Means algorithm is quite effective in identifying the initial pattern of student competence, with a Silhouette Score of 0.3489, which falls into the medium category. This study concludes that the use of the K-Means algorithm alone is sufficient to support the analysis of student areas of competence, with potential applications as a recommendation system for students in choosing elective courses and as an evaluation tool for study programs to identify areas of competence that need improvement.
AbstractList   This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas of competence as features. The K-Means algorithm is used to group student data into several clusters based on academic grade patterns. The results show that the K-Means algorithm is quite effective in identifying the initial pattern of student competence, with a Silhouette Score of 0.3489, which falls into the medium category. This study concludes that the use of the K-Means algorithm alone is sufficient to support the analysis of student areas of competence, with potential applications as a recommendation system for students in choosing elective courses and as an evaluation tool for study programs to identify areas of competence that need improvement.
Author Andini, Ratih Friska Dwi
Liantoni, Febri
Budianto, Aris
Author_xml – sequence: 1
  givenname: Ratih Friska Dwi
  surname: Andini
  fullname: Andini, Ratih Friska Dwi
– sequence: 2
  givenname: Febri
  surname: Liantoni
  fullname: Liantoni, Febri
– sequence: 3
  givenname: Aris
  surname: Budianto
  fullname: Budianto, Aris
BookMark eNotj8tKAzEARYNUsNZuXc8PZEwmb1yVwRdWXFjBXchkkjYwkylJKvj3ttXVvXDhcM81mMUpOgBuMaoJVkTclVB_YxFwTZHAF2DeMIkhlvxrduxIMkgZa67AMufQIUoFJ5KIObhvh0MuLoW4rT7KoXexVO007l1x0QaXq898msrOVa_wzZmYq9WwnVIou_EGXHozZLf8zwXYPD5s2me4fn96aVdraIXCsLGoN4pTQaVVDaX8-FYZqrj0jHfeC0wwUxZ1zCvEcIeUsL0hhiPZkd4IsgD1H9amKefkvN6nMJr0ozHSZ3ldgj7L65M8-QUCJE6Y
ContentType Journal Article
DBID AAYXX
CITATION
DOI 10.31937/ti.v17i1.4071
DatabaseName CrossRef
DatabaseTitle CrossRef
DatabaseTitleList CrossRef
DeliveryMethod fulltext_linktorsrc
EISSN 2581-186X
EndPage 106
ExternalDocumentID 10_31937_ti_v17i1_4071
GroupedDBID AAYXX
CITATION
M~E
ID FETCH-LOGICAL-c791-2c0da964748c924469379a4968f56bff713159c0b5f9051b097cda3a608b3da73
ISSN 2085-4552
IngestDate Sat Nov 29 07:44:02 EST 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed false
IsScholarly false
Issue 1
Language English
License http://creativecommons.org/licenses/by-sa/4.0
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c791-2c0da964748c924469379a4968f56bff713159c0b5f9051b097cda3a608b3da73
OpenAccessLink https://ejournals.umn.ac.id/index.php/TI/article/download/4071/1766
PageCount 8
ParticipantIDs crossref_primary_10_31937_ti_v17i1_4071
PublicationCentury 2000
PublicationDate 2025-07-01
PublicationDateYYYYMMDD 2025-07-01
PublicationDate_xml – month: 07
  year: 2025
  text: 2025-07-01
  day: 01
PublicationDecade 2020
PublicationTitle Ultimatics : Jurnal Teknik Informatika
PublicationYear 2025
SSID ssib044763837
Score 1.9133695
Snippet   This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the...
SourceID crossref
SourceType Index Database
StartPage 99
Title Clustering Student Competencies Using the K-Means Algorithm
Volume 17
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2581-186X
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssib044763837
  issn: 2085-4552
  databaseCode: M~E
  dateStart: 20090101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLbK4MAFMQ0EG0w5TNqhcskPJ7bFqfulaWgTQkXaLbITh1ntsqlNy07wr-_5OU07xmEcuESVk1hJ3tfP7yXvfY-QPRWpQsdlRqVmGWUVxKxaMkkNExDHue-FVYjNJvjFhbi8lF97vd_LWpjFhNe1uLuTt__V1DAGxnals_9g7m5SGIDfYHTYgtlh-yTDH07mTvzAK22jcCX-6dE5hrC475MEnL_5hZ4bWKn6w8mPm6ltrq7XPdXvk8ainOsM3xqceb91ZMa1HffbIqbGjjtWH7ryGEwN-AbjV-AR29lY9Y9-2i7nx2LDYjzmBELxbsfBvMRdyFNT--BNRJx2WastYbl2n5SlXpF2YPxYKiIaCexWuGJc_ghZnj59r6R2IY5QiuARxwNnoExAYweLiNto4ELS1Wq2_IL_xyLXpR5C0IMz5I3N8fzcnf-MPI95Kh2zn_86XhISY8C-AnVXu3vzwp84xacHl7Dm2Kx5KKPX5FUbWgRDD4lN0jP1Fvm8gkPQwiFYh0OAcAgADkELh6CDwxsyOjkeHZ7StmEGLbiMaFyEpXKVxUwUEFazDK5RKiYzUaWZrioeJeC8FqFOK6fKpkPJi1IlKguFTkrFk7dko76pzTsSKC21SaQpYlFBSCtFKnUWGuecKiWy9D3ZX95sfutlUfK_P9ntJx-5Q16uUPWBbDTTuflIXhSLxs6mu2iYewp2WXM
linkProvider ISSN International Centre
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Clustering+Student+Competencies+Using+the+K-Means+Algorithm&rft.jtitle=Ultimatics+%3A+Jurnal+Teknik+Informatika&rft.au=Andini%2C+Ratih+Friska+Dwi&rft.au=Liantoni%2C+Febri&rft.au=Budianto%2C+Aris&rft.date=2025-07-01&rft.issn=2085-4552&rft.eissn=2581-186X&rft.volume=17&rft.issue=1&rft.spage=99&rft.epage=106&rft_id=info:doi/10.31937%2Fti.v17i1.4071&rft.externalDBID=n%2Fa&rft.externalDocID=10_31937_ti_v17i1_4071
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2085-4552&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2085-4552&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2085-4552&client=summon