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Myers-Briggs Type Indicator Personality Model Classification in English Text using Convolutional Neural Network Method

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 15 No. 2 (2022) · pp. 93–103

DOI
10.21609/jiki.v15i2.1052
ISSN
2502-9274, 2088-7051
Published
2022-07-02
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Myers-Briggs Type Indicator (MBTI) is a personality model developed by Katharine Cooks Briggs and Isabel Briggs Myers in 1940. It displays a combination of preferences from four domains. Generally, test takers need to answer about 50 to 70 questions, and it is relatively expensive to know MBTI personality. The researcher developed a personality classification system using the Convolutional Neural Network (CNN) method and GloVe (Global Vectors for Word Representation) word embedding to solve this problem. The dataset used in this research consists of 8,675 data from the Kaggle site. The steps in this research are downloading the dataset from Kaggle, text preprocessing, GloVe weighting, classification using the CNN method, and evaluation using accuracy from the Confusion Matrix. Based on the tests carried out, using GloVe weighting can improve the model accuracy rather than random weighting. The best GloVe word dimensions depend on the metrics used to measure the model performance and the data of the classes contained in the dataset. From the CNN hyperparameter tuning test, the Adamax optimizer performs better and produces higher accuracy than the Adam optimizer. In addition, the CNN hyperparameter tuning increased model accuracy more significantly compared with the best GloVe word embedding dimensions.

Keywords

Collaboration network of the authors

40 authors · 71 co-authorship links · 2 across different organisations · showing the 40 most connected

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

  • University of Brawijaya
  • Binus University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Fajar PradanaUniversity of Brawijaya10
Retno Indah RokhmawatiUniversity of Brawijaya3
Fitri UtaminingrumUniversity of Brawijaya3
Bayu PriyambadhaUniversity of Brawijaya2
Satrio Agung WicaksonoUniversity of Brawijaya2
Achmad BasukiUniversity of Brawijaya2
Imam CholissodinUniversity of Brawijaya2
Muhammad WafiUniversity of Brawijaya2
Novanto YudistiraUniversity of Brawijaya2
Herman TolleUniversity of Brawijaya2
Aisyah AwalinaUniversity of Brawijaya2
Rizal Setya PerdanaUniversity of Brawijaya1
Nathanael Victor DarenohUniversity of Brawijaya1
Aditya RachmadiUniversity of Brawijaya1
Aulia Akhrian SyahidiUniversity of Brawijaya1
Fajerin BiabdillahUniversity of Brawijaya1
Nanang Yudi SetiawanUniversity of Brawijaya1
Iqbal Taufiq Ahmad NurUniversity of Brawijaya1
Indra SyahputraUniversity of Brawijaya1
Bayu SutawijayaUniversity of Brawijaya1
Mochammad Dearifaldi Al IkhsanUniversity of Brawijaya1
Sulandri SulandriUniversity of Brawijaya1
Rona SalsabilaUniversity of Brawijaya1
Muhammad ZulfikarrahmanUniversity of Brawijaya1
Diva Fardiana RisaUniversity of Brawijaya1
Aisyatul MaulidahUniversity of Brawijaya1
Intan Yusuf HabibieUniversity of Brawijaya1
Adam Syarif HidayatullahUniversity of Brawijaya1
Ovy RochmawantiUniversity of Brawijaya1
M. Sofyan IrwantoUniversity of Brawijaya1
Admi Rut SinanaUniversity of Brawijaya1
Kharis AlfianUniversity of Brawijaya1
Achmad RidokUniversity of Brawijaya1
Raga S. H. IstantoUniversity of Brawijaya1
Karina AmadeaUniversity of Brawijaya1
Gusti PangestuBinus University1
Budi Darma SetiawanUniversity of Brawijaya1
Muhammad Nugraha Delta RevanzaAffiliation 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) https://jiki.cs.ui.ac.id/index.php/jiki/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">Myers-Briggs Type Indicator Personality Model Classification in English Text using Convolutional Neural Network Method</dc:title>
	<dc:creator>Sugihdharma, Joseph Ananda</dc:creator>
	<dc:creator>Bachtiar, Fitra Abdurrachman</dc:creator>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">natural language processing</dc:subject>
	<dc:subject xml:lang="en-US">MBTI personality model</dc:subject>
	<dc:subject xml:lang="en-US">GloVe word embedding</dc:subject>
	<dc:subject xml:lang="en-US">convolutional neural network</dc:subject>
	<dc:description xml:lang="en-US">Myers-Briggs Type Indicator (MBTI) is a personality model developed by Katharine Cooks Briggs and Isabel Briggs Myers in 1940. It displays a combination of preferences from four domains. Generally, test takers need to answer about 50 to 70 questions, and it is relatively expensive to know MBTI personality. The researcher developed a personality classification system using the Convolutional Neural Network (CNN) method and GloVe (Global Vectors for Word Representation) word embedding to solve this problem. The dataset used in this research consists of 8,675 data from the Kaggle site. The steps in this research are downloading the dataset from Kaggle, text preprocessing, GloVe weighting, classification using the CNN method, and evaluation using accuracy from the Confusion Matrix. Based on the tests carried out, using GloVe weighting can improve the model accuracy rather than random weighting. The best GloVe word dimensions depend on the metrics used to measure the model performance and the data of the classes contained in the dataset. From the CNN hyperparameter tuning test, the Adamax optimizer performs better and produces higher accuracy than the Adam optimizer. In addition, the CNN hyperparameter tuning increased model accuracy more significantly compared with the best GloVe word embedding dimensions.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2022-07-02</dc:date>
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	<dc:format>text/plain</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1052</dc:identifier>
	<dc:identifier>10.21609/jiki.v15i2.1052</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 15 No. 2 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 93-103</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v15i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1052/467</dc:relation>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1052/483</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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