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Open access Jurnal SINTA 2

Word Analysis of Indonesian Keirsey Temperament

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 4 (2020) · pp. 365–376

DOI
10.22146/ijccs.58595
ISSN
2460-7258, 1978-1520
Published
2020-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Personality uniquely relates to our feeling and pattern to the aspect of actions. This behavior will change through the experience, formal education, and the surrounding environment. This works based on the Keirsey Temperament Sorter, a personality questionnaire developed by David Keirsey. This model divides the personality into four categories as Idealists, Rationals, Guardians, and Artisans. This concept is commonly recognized for the interpretation of specialist trends, potentially contributes to the process of recruitment or selection, and potential fields for analysis of social media data. Words selected by using Chi-Square with an error of 5%. Accuracy of the lexicon approach is 34%, while the best machine learning approach is Random Forest algorithm with 69.59%

Keywords

Collaboration network of the authors

25 authors · 51 co-authorship links · 3 across different organisations

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

  • Universitas Amikom Yogyakarta
  • Universitas AKPRIND Indonesia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anggit Dwi HartantoUniversitas Amikom Yogyakarta5
Wahyu HidayatUniversitas Amikom Yogyakarta4
Agung Budi PrasetyoUniversitas Amikom Yogyakarta2
Wahyu Krisna HidayatUniversitas Amikom Yogyakarta2
Agung Prasetio BudiUniversitas Amikom Yogyakarta2
Catur Riyono Heri WibowoUniversitas Amikom Yogyakarta1
HanafiAffiliation unknown1
Andi SunyotoUniversitas Amikom Yogyakarta1
Mursyid ArdiansyahUniversitas Amikom Yogyakarta1
Suwanto RaharjoUniversitas AKPRIND Indonesia1
Kartikasari Kusuma AgustiningsihUniversitas Amikom Yogyakarta1
Muhammad Altoumi AlsyaibaniUniversitas Amikom Yogyakarta1
Veny Cahya HarditaUniversitas Amikom Yogyakarta1
Emha Taufiq LuthfiUniversitas Amikom Yogyakarta1
Kusrini KusriniUniversitas Amikom Yogyakarta1
Arief SetyantoUniversitas Amikom Yogyakarta1
Mardi Siswo UtomoAffiliation unknown1
Mei KurniawanAffiliation unknown1
M. SuyantoAffiliation unknown1
KusriniAffiliation unknown1
Aiman MukhlishahAffiliation unknown1
Erwin SyahrudinUniversitas Amikom Yogyakarta1

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) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Word Analysis of Indonesian Keirsey Temperament</dc:title>
	<dc:creator>Iskandar, Ahmad Fikri</dc:creator>
	<dc:creator>Utami, Ema</dc:creator>
	<dc:creator>Prasetio, Agung Budi</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Keirsey; Temperament; Personality; Chi-Square</dc:subject>
	<dc:description xml:lang="en-US">Personality uniquely relates to our feeling and pattern to the aspect of actions. This behavior will change through the experience, formal education, and the surrounding environment. This works based on the Keirsey Temperament Sorter, a personality questionnaire developed by David Keirsey. This model divides the personality into four categories as Idealists, Rationals, Guardians, and Artisans. This concept is commonly recognized for the interpretation of specialist trends, potentially contributes to the process of recruitment or selection, and potential fields for analysis of social media data. Words selected by using Chi-Square with an error of 5%. Accuracy of the lexicon approach is 34%, while the best machine learning approach is Random Forest algorithm with 69.59%</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-10-31</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://journal.ugm.ac.id/ijccs/article/view/58595</dc:identifier>
	<dc:identifier>10.22146/ijccs.58595</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 4 (2020): October; 365-376</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/58595/29702</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>