skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Gender Classification for Anime Character Face Image Using Random Forest Classifier Method and GLCM Feature Extraction

Juita : jurnal informatika (Online), 2022-11, Vol.10 (2), p.243-250 [Peer Reviewed Journal]

ISSN: 2086-9398 ;EISSN: 2579-8901 ;DOI: 10.30595/juita.v10i2.13833

Full text available

Citations Cited by
  • Title:
    Gender Classification for Anime Character Face Image Using Random Forest Classifier Method and GLCM Feature Extraction
  • Author: Mulyana, Dadang Iskandar ; Pramansah, Vika Vitaloka
  • Subjects: random forest classifier, glcm, anime, classification, gender
  • Is Part Of: Juita : jurnal informatika (Online), 2022-11, Vol.10 (2), p.243-250
  • Description: Japan has many entertaining and unique artworks, especially its signature animation, called anime. Anime is an animation art that is unique in that the characterizations, characters, and storylines are made to resemble human life. The characters have 2 genders called male and female with unique visuals and are the characteristics of each anime character to entertain the audience. Training large-scale data and complex textures because not all of the anime images owned are of high quality, making classification by Machine Learning Algorithms low in accuracy. This study will describe an experiment using an anime face image dataset to classify the gender, namely male or female. From this problem, this research implements feature extraction to produce unique features of anime images with Gray-Level Cooccurrence Matrix (GLCM) and uses the Random Forest Classifier which is a classification algorithm in Machine Learning to classify gender. The results of this study get a good accuracy value of 95%, using 3,612 images where the test data used is 723 images and Homogeneity5 feature being the most relevant feature in increasing the accuracy value with a value of 0.06378389.
  • Publisher: Universitas Muhammadiyah Purwokerto
  • Language: English;Indonesian
  • Identifier: ISSN: 2086-9398
    EISSN: 2579-8901
    DOI: 10.30595/juita.v10i2.13833
  • Source: DOAJ Directory of Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait