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Prediksi Tipe Kepribadian MBTI Artis K-Pop Berdasarkan Caption Instagram Menggunakan Word2Vec dan Long-Short Term Memory (LSTM)

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 10 No. 5 (2023) · pp. 993–1002

DOI
10.25126/jtiik.2023107064
ISSN
2528-6579, 2355-7699
Published
2023-10-17
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
id
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Myers-Briggs Type Indicator (MBTI) adalah metode pengujian psikologi yang membedakan kepribadian seseorang. MBTI termasuk pembagian tipe kepribadian yang paling populer di dunia, termasuk di Korea Selatan. Tren MBTI di Korea Selatan juga dimanfaatkan oleh para artis K-Pop untuk berbagi tipe MBTI sehingga bisa mendekatkan hubungan antara penggemar dan idolanya. Salah satu media sosial yang umum digunakan oleh artis K-Pop adalah Instagram. Penelitian ini mencoba membuat model klasikasi tipe kepribadian berdasarkan caption Instagram artis K-Pop menggunakan Word2Vec dan Long-Short Term Memory (LSTM). Terdapat 118.401 data caption yang sudah dibersihkan melalui serangkaian langkah pre-processing dari 458 artis. Distribusi tipe kepribadian menunjukkan bahwa target tidak seimbang sehingga perlu dilakukan penanganan yaitu penggeseran threshold yang dilakukan pasca pemodelan. Evaluasi kombinasi model menghasilkan nilai macro f1 0,65 pada data artis, dengan rincian model Extroversion-Introversion, Sensing-Intuition, Thinking-Feeling memiliki nilai macro f1 yang sama yaitu 0,88, sedangkan model Judging-Perceiving memiliki nilai macro f1 yang sedikit lebih baik yaitu 0,90. Model diimplementasikan dalam aplikasi web Streamlit agar penggemar K-Pop dapat menggunakannya untuk memprediksi tipe MBTI dengan masukan caption Instagram. Aplikasi web dievaluasi menggunakan kuesioner System Usability Scale (SUS) dan mendapatkan skor 84,55 sehingga sudah termasuk kategori acceptable. Abstract Myers-Briggs Type Indicator (MBTI) is a psychological test that distinguishes a person's personality. The MBTI is one of the most popular personality types in the world, including South Korea. The MBTI trend in South Korea is also used by K-Pop artists to share their MBTI types so they could be closer to their fans. One of the social media commonly used by K-Pop artists is Instagram. This study tries to develop a personality type classification model based on Instagram captions of K-Pop artists using Word2Vec and Long-Short Term Memory (LSTM). There are 118,401 caption data that have been cleaned through a series of pre-processing steps from 458 artists. The distribution of personality types shows that the target is not balanced, so it is necessary to handle imbalanced data, namely shifting the threshold after modeling. Evaluation of the combination model yields a macro f1 value of 0.65 in the artist data, with the details of the Extroversion-Introversion, Sensing-Intuition, Thinking-Feeling models having the same macro f1 value of 0.88, while the Judging-Perceiving model has a slightly better macro f1 value of 0.90. The model is deployed in the Streamlit web application so that K-Pop fans can use it to predict the MBTI type by inputting Instagram captions. The web application is evaluated using the System Usability Scale (SUS) questionnaire and gets a score of 84.55 so it is considered acceptable.

Collaboration network of the authors

40 authors · 92 co-authorship links · 32 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
  • National Research and Innovation Agency
  • Institut Teknologi Sepuluh Nopember
  • Indonesian Institute of Sciences
  • Universitas Muhammadiyah Malang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Admaja Dwi HerlambangUniversity of Brawijaya5
Yusi Tyroni MursityoUniversity of Brawijaya3
Satrio Agung WicaksonoUniversity of Brawijaya3
Ahmad Afif SupiantoUniversity of Brawijaya3
Dian Eka RatnawatiUniversity of Brawijaya3
Fahrur RoziInstitut Teknologi Sepuluh Nopember2
Septiyan Andika IsantaUniversitas Muhammadiyah Malang2
Aditya RachmadiUniversity of Brawijaya2
Faizatul AmaliaUniversity of Brawijaya2
Welly PurnomoUniversity of Brawijaya2
Nur Laita Rizki AmaliaUniversity of Brawijaya1
Vicky ZilvanIndonesian Institute of Sciences1
Asri Rizki YulianiIndonesian Institute of Sciences1
Ade RamdanIndonesian Institute of Sciences1
Reza Andria SAffiliation unknown1
Sarwosri SarwosriInstitut Teknologi Sepuluh Nopember1
Diana PurwitasariInstitut Teknologi Sepuluh Nopember1
Yufis AzharUniversitas Muhammadiyah Malang1
Cindy Felita Nur AlimahUniversity of Brawijaya1
Fitra Abdurrachman BachtiarUniversity of Brawijaya1
Iqbal Taufiq Ahmad NurUniversity of Brawijaya1
Muhammad Rosyid MubarokUniversity of Brawijaya1
Anisah IkhsawiyanthiUniversity of Brawijaya1
Pratama ApriliantoUniversity of Brawijaya1
Hanifah Muslimah Az-ZahraUniversity of Brawijaya1
Alfansya Achmad MulyadiUniversity of Brawijaya1
Meilita Dwi AryatiUniversity of Brawijaya1
Azka AzkiyahUniversity of Brawijaya1
Abdul Harris WicaksonoUniversity of Brawijaya1
Didik KrisnandiNational Research and Innovation Agency1
Ana HeryanaNational Research and Innovation Agency1
Fawwaz Roja MahardikaUniversity of Brawijaya1
Raden Sandra YuwanaNational Research and Innovation Agency1
Endang SuryawatiNational Research and Innovation Agency1
Fatmawati FatmawatiUniversity of Brawijaya1
Tri AfiriantoUniversity of Brawijaya1
M. Ro'ifUniversity of Brawijaya1

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://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="id">Prediksi Tipe Kepribadian MBTI Artis K-Pop Berdasarkan Caption Instagram Menggunakan Word2Vec dan Long-Short Term Memory (LSTM)</dc:title>
	<dc:creator xml:lang="id">Hakim, Alfian</dc:creator>
	<dc:creator xml:lang="id">Wijoyo, Satrio Hadi</dc:creator>
	<dc:creator xml:lang="id">Setiawan, Nanang Yudi</dc:creator>
	<dc:description xml:lang="id">Myers-Briggs Type Indicator (MBTI) adalah metode pengujian psikologi yang membedakan kepribadian seseorang. MBTI termasuk pembagian tipe kepribadian yang paling populer di dunia, termasuk di Korea Selatan. Tren MBTI di Korea Selatan juga dimanfaatkan oleh para artis K-Pop untuk berbagi tipe MBTI sehingga bisa mendekatkan hubungan antara penggemar dan idolanya. Salah satu media sosial yang umum digunakan oleh artis K-Pop adalah Instagram. Penelitian ini mencoba membuat model klasikasi tipe kepribadian berdasarkan caption Instagram artis K-Pop menggunakan Word2Vec dan Long-Short Term Memory (LSTM). Terdapat 118.401 data caption yang sudah dibersihkan melalui serangkaian langkah pre-processing dari 458 artis. Distribusi tipe kepribadian menunjukkan bahwa target tidak seimbang sehingga perlu dilakukan penanganan yaitu penggeseran threshold yang dilakukan pasca pemodelan. Evaluasi kombinasi model menghasilkan nilai macro f1 0,65 pada data artis, dengan rincian model Extroversion-Introversion, Sensing-Intuition, Thinking-Feeling memiliki nilai macro f1 yang sama yaitu 0,88, sedangkan model Judging-Perceiving memiliki nilai macro f1 yang sedikit lebih baik yaitu 0,90. Model diimplementasikan dalam aplikasi web Streamlit agar penggemar K-Pop dapat menggunakannya untuk memprediksi tipe MBTI dengan masukan caption Instagram. Aplikasi web dievaluasi menggunakan kuesioner System Usability Scale (SUS) dan mendapatkan skor 84,55 sehingga sudah termasuk kategori acceptable.
 
Abstract
Myers-Briggs Type Indicator (MBTI) is a psychological test that distinguishes a person's personality. The MBTI is one of the most popular personality types in the world, including South Korea. The MBTI trend in South Korea is also used by K-Pop artists to share their MBTI types so they could be closer to their fans. One of the social media commonly used by K-Pop artists is Instagram. This study tries to develop a personality type classification model based on Instagram captions of K-Pop artists using Word2Vec and Long-Short Term Memory (LSTM). There are 118,401 caption data that have been cleaned through a series of pre-processing steps from 458 artists. The distribution of personality types shows that the target is not balanced, so it is necessary to handle imbalanced data, namely shifting the threshold after modeling. Evaluation of the combination model yields a macro f1 value of 0.65 in the artist data, with the details of the Extroversion-Introversion, Sensing-Intuition, Thinking-Feeling models having the same macro f1 value of 0.88, while the Judging-Perceiving model has a slightly better macro f1 value of 0.90. The model is deployed in the Streamlit web application so that K-Pop fans can use it to predict the MBTI type by inputting Instagram captions. The web application is evaluated using the System Usability Scale (SUS) questionnaire and gets a score of 84.55 so it is considered acceptable.</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2023-10-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jtiik.ub.ac.id/index.php/jtiik/article/view/7064</dc:identifier>
	<dc:identifier>10.25126/jtiik.2023107064</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 10 No 5: Oktober 2023; 993-1002</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 10 No. 5: Oktober 2023; 993-1002</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.2023105</dc:source>
	<dc:language>id</dc:language>
	<dc:relation>https://jtiik.ub.ac.id/index.php/jtiik/article/view/7064/1174</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>