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Open access Jurnal SINTA 2

Personality Classification of Myers Briggs Type Indicators (MBTI) Using BERT and Machine Learning

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 2 (2025) · pp. 129–140

DOI
10.22146/ijccs.104126
ISSN
2460-7258, 1978-1520
Published
2025-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Personality classification using textual data from social media or online forums is a complex task due to the unstructured text and the multifaceted nature of personality. While the Myers-Briggs Type Indicator (MBTI) provides a comprehensive framework, adapting it to media data and handling diverse linguistic patterns requires effective algorithms. The psychological basis of MBTI is intricate, especially when using complex methods like deep learning, which can be challenging. This study classifies personality types based on each individual's behavior on an online forum by observing the linguistic patterns of posted textual data using the SVM, Random Forest, BERT, and Word2Vec algorithms. The SVM and Random Forest algorithms are traditional machine learning algorithms known for their capabilities and effectiveness in text classification. Meanwhile, BERT and Word2Vec identify semantic relationships and contextual information from textual data. In addition, the IndoBERT model will be used for the BERT model because this study focuses on the classification of Indonesian language texts.Testing was carried out using textual data from posts on the PersonalityCafe forum. The test results showed that the combination of the SVM and IndoBERT models outperformed other models with an accuracy rate of 82% and an F1 score of 75%.

Keywords

Collaboration network of the authors

13 authors · 18 co-authorship links · 4 across different organisations

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

  • Universitas Gadjah Mada
  • Universitas Teknokrat Indonesia
  • Sebelas Maret University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Azhari SnUniversitas Gadjah Mada2
Yohanes SuyantoUniversitas Gadjah Mada2
Abram Setyo PrabowoUniversitas Gadjah Mada1
Yayang Eluis Bali MawartikaUniversitas Gadjah Mada1
Muhammad Asaduddin HazaziUniversitas Gadjah Mada1
Auliya Rahman IsnainUniversitas Teknokrat Indonesia1
Sri T. HartatiUniversitas Gadjah Mada1
Ainil MardiahUniversitas Gadjah Mada1
Annisaa UtamiUniversitas Gadjah Mada1
Astrid Noviana ParadhitaSebelas Maret University1
Anny Kartika SariUniversitas Gadjah Mada1

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">Personality Classification of Myers Briggs Type Indicators (MBTI) Using BERT and Machine Learning</dc:title>
	<dc:creator>Sihabuddin, Agus</dc:creator>
	<dc:creator>Ekapratiwi, Dian</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">BERT, MBTI, Personality Classification, Random Forest, SVM</dc:subject>
	<dc:description xml:lang="en-US">Personality classification using textual data from social media or online forums is a complex task due to the unstructured text and the multifaceted nature of personality. While the Myers-Briggs Type Indicator (MBTI) provides a comprehensive framework, adapting it to media data and handling diverse linguistic patterns requires effective algorithms. The psychological basis of MBTI is intricate, especially when using complex methods like deep learning, which can be challenging.      This study classifies personality types based on each individual's behavior on an online forum by observing the linguistic patterns of posted textual data using the SVM, Random Forest, BERT, and Word2Vec algorithms. The SVM and Random Forest algorithms are traditional machine learning algorithms known for their capabilities and effectiveness in text classification. Meanwhile, BERT and Word2Vec identify semantic relationships and contextual information from textual data. In addition, the IndoBERT model will be used for the BERT model because this study focuses on the classification of Indonesian language texts.Testing was carried out using textual data from posts on the PersonalityCafe forum. The test results showed that the combination of the SVM and IndoBERT models outperformed other models with an accuracy rate of 82% and an F1 score of 75%.</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>2025-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/104126</dc:identifier>
	<dc:identifier>10.22146/ijccs.104126</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 2 (2025): April; 129-140</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/104126/41523</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 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>
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