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

Sukemi, Sukemi and Samsuryadi, Samsuryadi and Supardi, Julian (2023) A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology. Journal of Electrical and Computer Engineering, 2023. ISSN 2090/0147

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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 classifcation techniques to get maximum accuracy. Terefore, this study aims to design a reliable framework using image processing and machine learning approaches such as fltering, thresholding, and normalization to determine the personality traits through handwriting features. Ten, handwriting features are classifed according to the Big Five model. Experiments using the decision tree, SVM (kernel RBF), and KNN produced an accuracy above 99%. Tese 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) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Dr. Sukemi Sukemi
Date Deposited:	12 Apr 2023 00:53
Last Modified:	17 Apr 2023 02:17
URI:	http://repository.unsri.ac.id/id/eprint/95784

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