Klasifikasi Kepribadian Berdasarkan Dimensi Ekstraversi Berbasis Data Mining Menggunakan Extremely Randomized Trees

Penulis

  • Yanuardi Yanuardi Universitas Muhammadiyah Tangerang
  • Firdiansyah Firdaus Basri Universitas Muhammadiyah Tangerang
  • Firdiansyah Firdaus Basri Universitas Muhammadiyah Tangerang
  • Muhammad Luthfi Aksani Universitas Muhammadiyah Tangerang
  • Muhammad Luthfi Aksani Universitas Muhammadiyah Tangerang

DOI:

https://doi.org/10.22441/format.2025.v14.i2.008

Kata Kunci:

Personality Classification, Extraversion Dimension, Data Mining, Extremely Randomized Trees, Machine Learning

Abstrak

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.

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Referensi

Z. Zubaidah, F. F. Triana, G. Ananta, R. D. Sadewa, and R. Arkhan, “Konsep Dasar Tes Five Big Personality Traits pada Kepribadian Siswa,” in SENJA KKN (Seminar dalam Jaringan Konseling Kearifan Nusantara), 2024, pp. 280–290.

M. M. Karundeng, S. L. Mandey, and R. N. Taroreh, “Pengaruh Kepribadian Ekstraversi dan Gaya Kepemimpinan Transformasional Terhadap Kinerja Pegawai di Kecamatan Ranowulu Kota Bitung,” J. EMBA, vol. 10, no. 1, pp. 1030–1040, 2022.

N. F. Ainun, N. Nurhikmah, and A. M. Aditya, “Hubungan Antara Kecenderungan Tipe Kepribadian Extraversion dengan Cyberloafing pada Mahasiswa di Kota Makassar,” J. Psikol. Karakter, vol. 4, no. 2, pp. 420–427, 2024, doi: 10.56326/jpk.v4i2.3725.

M. P. Pulungan, A. Purnomo, and A. Kurniasih, “Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Kepribadian MBTI Menggunakan Naive Bayes Classifier,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 5, pp. 1033–1042, 2024, doi: 10.25126/jtiik.2024117989.

P. J. Aliffiyah and N. Pratiwi, “Deteksi Tipe Sidik Jari Untuk Mengenali Kepribadian Menggunakan Metode Support Vector Machine (SVM),” Metik J., vol. 9, no. 2, pp. 375–384, 2025, doi: 10.47002/metik.v9i2.1073.

A. Oktafiqurahman, K. Kusrini, and A. Nasiri, “Prediksi Kepribadian Berdasarkan Status Sosial Media Facebook Menggunakan Metode Naive Bayes dan KNN,” J. Teknol. Inf. dan Komun., vol. 11, no. 2, pp. 30–34, 2023, doi: 10.30646/tikomsin.v11i2.747.

A. Subtinanda and N. Yuliana, “Kepribadian Ekstrovert dan Introvert dalam Konteks Komunikasi Antarpribadi Mahasiswa Ilmu Komunikasi UNTIRTA,” J. Pendidik. Non Form., vol. 1, no. 2, p. 15, 2023, doi: 10.47134/jpn.v1i2.187.

M. Prasetio, H. Sulistiani, O. Y. Inonu, K. Magda, and B. Santosa, “Klasifikasi Tingkat Risiko Gempa Menggunakan Pola Spasial dan Temporal Berbasis Decision Tree Mugi,” Bull. Comput. Sci. Res., vol. 5, no. 5, pp. 1059–1066, 2025, doi: 10.47065/bulletincsr.v5i5.624.

M. Rizky, P. Soewarno, R. Ardianto, R. Suryani, R. R. Al-hakim, and I. Wahyudi, “Analisis Perbandingan Algoritma KNN dan SVM untuk Prediksi Risiko Kesehatan Ibu,” J. Kolaborasi Ris. Sarj., vol. 2, no. 3, pp. 1–8, 2025.

S. D. Wahyuni and R. H. Kusumodestoni, “Optimalisasi Algoritma Support Vector Machine (SVM) Dalam Klasifikasi Kejadian Data Stunting,” Bull. Inf. Technol., vol. 5, no. 2, pp. 56–64, 2024, doi: 10.47065/bit.v5i2.1247.

D. Al Mahkya, K. A. Notodiputro, and B. Sartono, “Extra Trees Method for Stock Price Forecasting With Rolling Origin Accuracy Evaluation,” Media Stat., vol. 15, no. 1, pp. 36–47, 2022, doi: 10.14710/medstat.15.1.36-47.

L. Nur Aina, V. R. S. Nastiti, and C. S. K. Aditya, “Implementasi Extra Trees Classifier dengan Optimasi Grid Search CV pada Prediksi Tingkat Adaptasi,” MIND (Multimedia Artif. Intell. Netw. Database) J., vol. 9, no. 1, pp. 78–88, 2024, [Online]. Available: https://doi.org/10.26760/mindjournal.v9i1.78-88

R. Kapilavayi, “Extrovert vs. Introvert Behavior Data,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/rakeshkapilavai/extrovert-vs-introvert-behavior-data

A. Sah, C. Niesa, R. R. Jafar, and M. Muharrom, “Analisis Model Prediksi Penyakit Jantung Menggunakan Adaptive Boosting, Gradient Boosting, dan Extreme Gradient Boosting,” J. Ilm. FIFO, vol. 17, no. 1, pp. 46–56, 2025, doi: 10.22441/fifo.2025.v17i1.006.

W. Aprilita, Junadhi, Agustin, and H. Asnal, “Analisis Sentimen Layanan Hotel Menggunakan Algoritma Extra Trees: Studi Kasus pada Ulasan Pelanggan,” Indones. J. Comput. Sci., vol. 13, no. 3, pp. 4642–4653, 2024, doi: 10.33022/ijcs.v13i3.4014.

A. Aminifar, M. Shokri, F. Rabbi, V. K. I. Pun, and Y. Lamo, “Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data,” IEEE Access, vol. 10, pp. 6010–6027, 2022, doi: 10.1109/ACCESS.2022.3141709.

R. K. Rachmansyah and R. Astriratma, “Implementasi Algoritma Extra Trees Untuk Klasifikasi Cuaca Provinsi DKI Jakarta Dengan Oversampling SMOTE,” in Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA), 2023, pp. 461–472.

U. Saeed, S. U. Jan, Y.-D. Lee, and I. Koo, “Fault diagnosis based on extremely randomized trees in wireless sensor networks,” Reliab. Eng. Syst. Saf., vol. 205, no. 107284, 2021, doi: https://doi.org/10.1016/j.ress.2020.107284.

A. Aminifar, F. Rabbi, K. I. Pun, and Y. Lamo, “Privacy preserving distributed extremely randomized trees,” in Proceedings of the 36th Annual ACM Symposium on Applied Computing, in SAC ’21. New York, NY, USA: Association for Computing Machinery, 2021, pp. 1102–1105. doi: 10.1145/3412841.3442110.

Parjito, I. Ahmad, R. I. Borman, A. D. Alexander, and Y. Jusman, “Combining Extreme Learning Machine and Linear Discriminant Analysis for Optimized Apple Leaf Disease Classification,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), IEEE, 2024, pp. 138–143. doi: 10.1109/ICE3IS62977.2024.10775844.

R. Rusliyawati, K. Karnadi, A. M. Tanniewa, A. C. Widyawati, Y. Jusman, and R. I. Borman, “Detection of Pepper Leaf Diseases Through Image Analysis Using Radial Basis Function Neural Networks,” in BIO Web of Conferences, 2024, pp. 1–10. doi: 10.1051/bioconf/202414401005.

F. S. Nahm, “Receiver operating characteristic curve: overview and practical use for clinicians,” Korean J. Anesthesiol., vol. 75, no. 1, pp. 25–36, 2022, doi: 10.4097/kja.21209.

Diterbitkan

2025-09-26

Cara Mengutip

[1]
Y. Yanuardi, F. F. Basri, F. F. Basri, M. L. Aksani, dan M. L. Aksani, “Klasifikasi Kepribadian Berdasarkan Dimensi Ekstraversi Berbasis Data Mining Menggunakan Extremely Randomized Trees”, FORMAT, vol. 14, no. 2, hlm. 229–237, Sep 2025.

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