A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology

Samsuryadi, Samsuryadi and Julian, Supardi and Sukemi, Sukemi (2023) A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology. Journal of Electrical and Computer Engineering, 2023 (1). pp. 1-15. ISSN 2090-0155

Text (Artikel) ARTIKEL - A Framework for Determining.pdf - Published Version Download (1MB)

Abstract

Along with the progress of the times, the development of graphology has changed towards computerization. The fundamental problem in automated graphology is how to determine personality traits through digital handwriting using the principles of graphology. Although various models and approaches have been developed in research related to automated graphology, there are still obstacles to overcome such as the selection of preprocessing techniques and image processing algorithms to extract handwriting features and proper classification techniques to get maximum accuracy. Therefore, this study aims to design a reliable framework using image processing and machine learning approaches such as filtering, thresholding, and normalization to determine the personality traits through handwriting features. Then, handwriting features are classified according to the Big Five model. Experiments using the decision tree, SVM (kernel RBF), and KNN produced an accuracy above 99%. These results indicated that the proposed framework can be well applied to predict the personality of the Big Five model through handwriting analysis features.

Item Type:	Article
Subjects:	Q Science > Q Science (General) > Q1-295 General
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Dr. Syamsuryadi Sahmin
Date Deposited:	13 Apr 2023 06:20
Last Modified:	13 Apr 2023 06:20
URI:	http://repository.unsri.ac.id/id/eprint/96263

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