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All Journal Jurnal Civics: Media Kajian Kewarganegaraan INFORMASI Jurnal Natapraja : Kajian Ilmu Administrasi Negara Format : Jurnal Imiah Teknik Informatika JURNAL TEKNIK JIKA (Jurnal Informatika) Bulletin of Computer Science Research Insearch: Information System Research Journal Journal of Indonesian Management J-Intech (Journal of Information and Technology) Scientica: Jurnal Ilmiah Sains dan Teknologi Causa: Jurnal Hukum dan Kewarganegaraan JEITECH (JOURNAL OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY)
Yanuardi Yanuardi
Universitas Ibn Khaldun Bogor
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Journal : Format : Jurnal Imiah Teknik Informatika

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Klasifikasi Kepribadian Berdasarkan Dimensi Ekstraversi Berbasis Data Mining Menggunakan Extremely Randomized Trees Yanuardi, Yanuardi; Basri, Firdiansyah Firdaus; Aksani, Muhammad Luthfi
FORMAT Vol 14, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i2.008

Abstract

Personality is one of the fundamental aspects that distinguishes individual behavior, thought patterns, and interaction styles. The extraversion dimension, which is part of the Big Five Personality Traits framework, reflects an individual’s tendency to engage in social interactions with two main poles, namely introvert and extrovert. Identifying personality based on this dimension has various applications, ranging from education to employee recruitment. This study aims to develop a personality classification model based on the extraversion dimension using the Extremely Randomized Trees (ERT) algorithm and to compare its performance with other algorithms, namely Decision Tree, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The dataset used in this study was obtained from the Kaggle platform, consisting of 2,900 entries and including social behavior indicators represented by five numerical variables and two categorical variables. The research methodology involves data preprocessing, exploratory data analysis, model construction, and evaluation using confusion matrix, precision, recall, F1-score, accuracy, and ROC-AUC. The results indicate that ERT achieves the best performance compared to the other algorithms. The ERT model obtained an accuracy of 92.69%, an F1-score of 0.9269, and a ROC-AUC of 0.9429, outperforming SVM (F1 0.9173; AUC 0.9300), KNN (F1 0.9086; AUC 0.9146), and Decision Tree (F1 0.8879; AUC 0.8876). The superiority of ERT is supported by its tree-based ensemble mechanism with high randomization, which enhances generalization, reduces variance, and captures complex non-linear interactions among behavioral variables. Therefore, ERT is proven to be effective in consistently distinguishing introvert and extrovert tendencies.
Co-Authors Abdul Madjid Agung Budi Muljono Agung Wibowo Agus Supriyadi Ahmad Qaulan Syadid Ahmad Soleh Aksani, Muhammad Luthfi Alexander, Allan Desi Alfry Aristo Jansen Sinlae Ali Firdaus Anggalini, Titis Dewi Arisantoso Arisantoso Azhari, Lukman Damarsiwi, Eska Prima Monique Destriana, Rachmat Eko Wiyono Fajar, Kurnia Nur Faridi Faridi Fauyhi Eko Nugroho, Fauyhi Eko Fauzi, Hafiz Firdiansyah Firdaus Basri FITRIA NURAINI Fitriana, Kurnia Nur Handayani, Nurdiana hardiansyah, alim Harjian, Muhammad Rivaldi Hartaya, Kendra Herawati Herawati Heriyani, Nofitri Herlina, Rina Huasin, Syepry Maulana Karyadi, Bambang Layina, Andiny Lena Satlita Marita Ahdiyana Moh. Erkamim Mohammad Imam Shalahudin Mulyadi Mulyadi Nugroho, Aryanto Priyanggodo, Dyas Yudi Rohmat Taufiq Rusdianto, Hengki Sigit Wibowo Ulpiani, Ria Utami Dewi ZF Badoh, Ibrahim