Abstract
Psikologi atau kondisi kepribadian adalah faktor utama penilaian dalam diri manusia. Permasalahan utamanya adalah manusia secara alami akan menyembunyikan sifatnya. Sehingga terlihat sempurna dalam pengamatan orang lain. Selain itu, apabila manusia melakukan uji psikologi cenderung menjawab dengan jawaban yang sempurna. Dalam artian manipulatif supaya telihat sempurna. Berdasarkan hal tersebut, penelitian ini akan menggunakan metode penelitian berbasis Machine Learning untuk menangkap pola tersembunyi pada jawaban soal psikologi. Metode yang diujikan adalah KNN, Adaboost, Random Forest, SVM, Naive Bayes dan Decission Tree. Beberapa metode tersebut telah diujikan dan didapatkan metode terbaik adalah SVM dengan evaluasi precission 86,4% dan recall 88,1%. Berdasarkan hasil penelitian, metode machine learning dapat menyimpulkan pola tersembunyi dengan baik. Sehingga pendekatan ini dapat digunakan untuk melakukan analisis lebih dalam pada kasus kondisi kepribadaian atau psikologi.
References
[1] Furnham and H. Cheng, “The Big‐Five personality factors, cognitive ability, health, and social‐demographic indicators as independent predictors of self‐efficacy: A longitudinal study”, Scand. J. Psychol., vol. 65, no. 1, pp. 53–60, 2024.
[2] J. Hopwood, A. L. Pincus, and A. G. Wright, “Six assumptions of contemporary integrative interpersonal theory of personality and psychopathology”, Curr. Opin. Psychol., vol. 41, pp. 65–70, 2021.
[3] V. Burtaverde and C. Ene, “Individuals high on the Dark Triad traits choose to stay single if they are low on sociosexuality” , Pers. Individ. Differ, vol. 177, p. 110843, 2021.
[4] M. Monaro et al., “Detecting faking-good response style in personality questionnaires with four choice alternatives” , Psychol. Res, pp. 1–14, 2021.
[5] Y. Chen, W. Zhang, X. Wang, and H. Liu, “The impact of dataset shift on machine learning models in psychological assessment: A comparative study” , Comput. Human Behav, vol. 145, p. 107289, 2023, doi: 10.1016/j.chb.2023.107289.
[6] S. Kim, T. Park, and M. Lee, “Temporal dynamics as indicators of faking in self-report questionnaires: A deep learning approach” , Comput. Human Behav, vol. 147, p. 107398, 2023, doi: 10.1016/j.chb.2023.107398.
[7] P. Acevedo, E. N. Aron, S. Pospos, S. Jessim, and A. Aron, “Sensory processing sensitivity and its relation to personality and cognition” , Pers. Individ. Differ, vol. 171, p. 110485, 2021, doi: 10.1016/j.paid.2020.110485.
[8] T. P. Alloway and R. G. Alloway, “Working memory and academic achievement: An update” , Educ. Psychol. Rev, vol. 32, no. 2, pp. 473–493, 2020, doi: 10.1007/s10648-019-09508-8.
[9] Fernandez-Martinez, J. C. Mico, S. Edo, and M. Tarraga, “Perfectionism and mental health symptoms in university students” , J. Psychopathol. Behav. Assess, vol. 42, no. 2, pp. 221–231, 2020, doi: 10.1007/s10862-020-09793-8.
[10] N. W. Hudson and R. C. Fraley, “Personality structure across the lifespan: The emergence of Big Two and Big Five traits in adulthood” , J. Pers. Soc. Psychol, vol. 118, no. 3, pp. 477–506, 2020, doi: 10.1037/pspp0000234.
[11] Mancuso, A. Tamburello, A. Gagliano, P. Cali, and S. Costa, “Obsessive-compulsive personality disorder and emotional regulation difficulties: A systematic review” , Int. J. Environ. Res. Public Health, vol. 19, no. 21, p. 13878, 2022, doi: 10.3390/ijerph192113878.
[12] C. Rettew et al., “Assessing child and adolescent obsessive-compulsive personality traits using the Child and Adolescent Dysregulation Inventory (CADI)” , J. Clin. Psychol, vol. 76, no. 5, pp. 832–847, 2020, doi: 10.1002/jclp.22932.
[13] J. Stoeber, “The basics of perfectionism” , Curr. Dir. Psychol. Sci, vol. 30, no. 1, pp. 3–8, 2021, doi: 10.1177/0963721420958896.
[14] M. Rizki, M. Arhami, and H. Huzeni, “Perbaikan algoritma naive bayes classifier menggunakan teknik Laplacian Correction” , J. Teknol, vol. 21, no. 1, pp. 39–45, 2021.
[15] Yuniardini and T. Widiyaningtyas, “Analisis Perbandingan Pearson Correlation dan Cosine Similarity pada Rekomendasi Musik berbasis Collaborative Filtering” , Edumatic: J. Pendidik. Informatika, vol. 8, no. 2, pp. 555–564, 2024.
[16] W. Wijiyanto, A. I. Pradana, S. Sopingi, and V. Atina, “Teknik K-Fold Cross Validation untuk Mengevaluasi Kinerja Mahasiswa” , J. Algoritma, vol. 21, no. 1, pp. 239–248, 2024.
[17] Masruriyah et al., “Pengukuran Kinerja Model Klasifikasi dengan Data Oversampling pada Algoritma Supervised Learning untuk Penyakit Jantung” , Comput. Sci. (CO-SCIENCE), vol. 4, no. 1, pp. 62–70, 2024.

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