Word Analysis of Indonesian Keirsey Temperament


Ahmad Fikri Iskandar(1*), Ema Utami(2), Agung Budi Prasetio(3)

(1) AMIKOM University Yogyakarta
(2) AMIKOM University Yogyakarta
(3) AMIKOM University Yogyakarta
(*) Corresponding Author


Personality uniquely relates to our feeling and pattern to the aspect of actions. This behavior will change through the experience, formal education, and the surrounding environment. This works based on the Keirsey Temperament Sorter, a personality questionnaire developed by David Keirsey. This model divides the personality into four categories as Idealists, Rationals, Guardians, and Artisans. This concept is commonly recognized for the interpretation of specialist trends, potentially contributes to the process of recruitment or selection, and potential fields for analysis of social media data. Words selected by using Chi-Square with an error of 5%. Accuracy of the lexicon approach is 34%, while the best machine learning approach is Random Forest algorithm with 69.59%


Keirsey; Temperament; Personality; Chi-Square

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[1] L. C. Lukito, A. Erwin, J. Purnama, and W. Danoekoesoemo, “Social media user personality classification using computational linguistic,” Proc. 2016 8th Int. Conf. Inf. Technol. Electr. Eng. Empower. Technol. Better Futur. ICITEE 2016, no. October 2016, 2017, doi: 10.1109/ICITEED.2016.7863313.

[2] E. Utami, A. D. Hartanto, S. Adi, I. Oyong, and S. Raharjo, “Profiling analysis of DISC personality traits based on Twitter posts in Bahasa Indonesia,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2019, doi: 10.1016/j.jksuci.2019.10.008.

[3] E. Susilawati, H. Sitompul, and J. Situmorang, “The Differences in Using Direct Instruction (DI) Learning Strategy Based on Competitive Behavior to Civic Education Learning Achivement,” no. January 2018, 2018, doi: 10.2991/aisteel-18.2018.47.

[4] D. Preoţiuc-Pietro et al., “The role of personality, age, and gender in tweeting about mental illness,” pp. 21–30, 2015, doi: 10.3115/v1/w15-1203.

[5] D. Keirsey, Please Understand Me II: Temperament, Character, Intelligence. 1998.

[6] A. L. Hammer, “Myers-Briggs type indicator. comparison report : Work styles,” pp. 1–13, 2015, [Online]. Available: https://www.cpp.com/pdfs/smp261182.pdf.

[7] G. Y. N. N. Adi, M. H. Tandio, V. Ong, and D. Suhartono, “Optimization for Automatic Personality Recognition on Twitter in Bahasa Indonesia,” Procedia Comput. Sci., vol. 135, pp. 473–480, 2018, doi: 10.1016/j.procs.2018.08.199.

[8] A. C. E. S. Lima and L. N. De Castro, “Tecla: A temperament and psychological type prediction framework from Twitter data,” PLoS One, vol. 14, no. 3, pp. 1–18, 2019, doi: 10.1371/journal.pone.0212844.

[9] M. Fikry, “Ekstrover atau Introver : Klasifikasi Kepribadian Pengguna Twitter dengan Menggunakan Metode Support Vector Machine,” J. Sains dan Teknol. Ind., vol. 16, no. 1, p. 72, 2018, doi: 10.24014/sitekin.v16i1.5326.

[10] V. Ong et al., “Personality prediction based on Twitter information in Bahasa Indonesia,” Proc. 2017 Fed. Conf. Comput. Sci. Inf. Syst. FedCSIS 2017, vol. 11, pp. 367–372, 2017, doi: 10.15439/2017F359.

[11] N. H. Jeremy, C. Prasetyo, and D. Suhartono, “Identifying personality traits for Indonesian user from twitter dataset,” Int. J. Fuzzy Log. Intell. Syst., vol. 19, no. 4, pp. 283–289, 2019, doi: 10.5391/IJFIS.2019.19.4.283.

[12] A. F. Iskandar and E. Utami, “Impact of Feature Extraction and Feature Selection Using Naïve Bayes on Indonesian Personality Trait,”. ICOIACT. Pending Publication.

[13] Y. I. Claudy, R. S. Perdana, and M. A. Fauzi, “Klasifikasi Dokumen Twitter Untuk Mengetahui Karakter Calon Karyawan Menggunakan Algoritme K-Nearest Neighbor (KNN),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 8, pp. 2761–2765, 2018, [Online]. Available: https://www.researchgate.net/publication/322959490.

[14] R. K. Roul, J. K. Sahoo, and K. Arora, “Modified TF-IDF Term Weighting Strategies for Text Categorization,” 2017 14th IEEE India Counc. Int. Conf. INDICON 2017, no. October, 2018, doi: 10.1109/INDICON.2017.8487593.

[15] J. R. Chang, H. Y. Liang, L. S. Chen, and C. W. Chang, “Novel feature selection approaches for improving the performance of sentiment classification,” J. Ambient Intell. Humaniz. Comput., no. 0123456789, 2020, doi: 10.1007/s12652-020-02468-z.

[16] Willy, E. B. Setiawan, and F. N. Nugraha, “Implementation of Decision Tree C4.5 for Big Five Personality Predictions with TF-RF and TF-CHI2 on Social Media Twitter,” 2019 Int. Conf. Comput. Control. Informatics its Appl. Emerg. Trends Big Data Artif. Intell. IC3INA 2019, pp. 114–119, 2019, doi: 10.1109/IC3INA48034.2019.8949601.

[17] M. Kosinski, D. Stillwell, and T. Graepel, “Private traits and attributes are predictable
from digital records of human behavior,” Proceedings of the National Academy of
Sciences, 110(15), 5802–5805, doi:10.1073/pnas.1218772110

DOI: https://doi.org/10.22146/ijccs.58595

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