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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 OPTIMIZATION

Created by :
Iksan Ramadhan ( 0321029501 )
Habibullah Akbar ; Gerry Firmansyah ; Agung Mulyo Widodo



SubjectKARAKTER
PERSONAL
Alt. Subject PERSONALITY
CHARACTER
KeywordMEDIA 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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