Zuriani, Mustaffa and Nur Alia Shahira, Mohd Zaidi and Ernawan, Ferda and Elhadi, Haithm and Hakim, Muhammad Malik (2022) Personality predictive analysis based on artificial neural network. In: Proceedings - International Conference on Informatics and Computational Sciences; 6th International Conference on Informatics and Computational Sciences, ICICoS 2022 , 28-29 September 2022 , Virtual, Online. pp. 105-110., 2022 (183902). ISSN 2767-7087 ISBN 978-166546099-6
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Abstract
Individual personality is a vital criterion in a human life as well as in a development of an organization. Every individual holds a different personality type with various characteristics in terms of thinking, feeling, and behaving. Due to that matter, analyzing human personality should be done carefully which can be challenging and time consuming. Therefore, this study presents a personality prediction model based on Artificial Neural Network (ANN). The prediction was realized on the Big Five personality model. The dataset consists of 709 rows and retrieved from open-source website called Kaggle. For training, validation and testing phases, the dataset was divided into 70:15:15 respectively. Findings of the study demonstrated the capability of ANN in producing better accuracy compared to three identified algorithms which includes Naïve Bayes, ZeroR and Random Forest.
Item Type: | Conference or Workshop Item (Lecture) |
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Additional Information: | Indexed by Scopus |
Uncontrolled Keywords: | Artificial neural network; Big five personality; Personality prediction |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) > T201 Patents. Trademarks T Technology > TA Engineering (General). Civil engineering (General) |
Faculty/Division: | Institute of Postgraduate Studies College of Engineering Faculty of Computing |
Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
Date Deposited: | 20 Nov 2023 07:16 |
Last Modified: | 20 Nov 2023 07:16 |
URI: | http://umpir.ump.edu.my/id/eprint/39336 |
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