A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/1249004 |
| _version_ | 1811171045658329088 |
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| author | null Samsuryadi Rudi Kurniawan Julian Supardi null Sukemi Fatma Susilawati Mohamad |
| author_facet | null Samsuryadi Rudi Kurniawan Julian Supardi null Sukemi Fatma Susilawati Mohamad |
| author_sort | null Samsuryadi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T17:07:28Z |
| format | Article |
| id | doaj.art-8daeacbdf6404a788e0bc56e8ca7872f |
| institution | Directory Open Access Journal |
| issn | 2090-0155 |
| language | English |
| last_indexed | 2024-04-10T17:07:28Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj.art-8daeacbdf6404a788e0bc56e8ca7872f2023-02-06T01:40:27ZengHindawi LimitedJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/1249004A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphologynull Samsuryadi0Rudi Kurniawan1Julian Supardi2null Sukemi3Fatma Susilawati Mohamad4Department of InformaticsDepartment of Engineering ScienceDepartment of InformaticsDepartment of Computer EngineeringFaculty of Informatics and ComputingAlong 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.http://dx.doi.org/10.1155/2023/1249004 |
| spellingShingle | null Samsuryadi Rudi Kurniawan Julian Supardi null Sukemi Fatma Susilawati Mohamad A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology Journal of Electrical and Computer Engineering |
| title | A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology |
| title_full | A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology |
| title_fullStr | A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology |
| title_full_unstemmed | A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology |
| title_short | A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology |
| title_sort | framework for determining the big five personality traits using machine learning classification through graphology |
| url | http://dx.doi.org/10.1155/2023/1249004 |
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