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UEU » Journal » Teknik Informatika Posted by [email protected] at 03/03/2021 23:33:56 • 344 Views
PREDICTIONS OF SIX PERSONALITY CHARACTERS (HEXACO) FROM SOCIAL MEDIA USING RANDOM FOREST CLASSIFIER AND PARTICLE SWARM OPTIMIZATIONCreated by :
Iksan Ramadhan ( 0321029501 ) Habibullah Akbar ; Gerry Firmansyah ; Agung Mulyo Widodo
Subject: | KARAKTER PERSONAL | Alt. Subject : | PERSONALITY CHARACTER | Keyword: | MEDIA SOSIAL PEKERJAAN |
Alt. Description
Personality assessment is important to assess whether a person would be appropriate
for a position in accordance to his/her personality. Traditionally, personality assessment
is evaluated based on Myers�Briggs Type Indicator. However, this model is considered
as too rigid and dichotomous. In this study, we used HEXACO model for personality
characterization based on their social media comments and textual interactions. We clean
these data by cleaning stopwords and interpreting the emoticon. Then, the features is
extracted using TF-IDF. The features then classified using Random Forest Classifier
which is optimized by Particle Swarm Optimization. The results show that the accuracy
can reach up to 90%. This shows that personality assessment can be performed in a more
convenient manner based on the applicant s social media comments and interactions (not
necessarily has to be conducted based on traditional personality assessment test).
Date Create | : | 03/03/2021 | Type | : | Text | Format | : | pdf | Language | : | Indonesian | Identifier | : | UEU-Journal-11_0950 | Collection ID | : | 11_0950 |
Source : Future ICT, Taiwan. 2021
Relation Collection: Fakultas Ilmu Komputer
Coverage : Civitas Akademika Universitas Esa Unggul
Rights : @2021 Perpustakaan Universitas Esa Unggul
Publication URL : https://digilib.esaunggul.ac.id/predictions-of-six-personality-characters-hexaco-from-social-media-using-random-forest-classifier-and-particle-swarm-optimization-19176.html
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UEU-Journal-19176-11_0950.pdf - 848 KB
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