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Indah Feristyani
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Computer Science & IT

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Klasifikasi Wajah Hewan Mamalia Tampak Depan Menggunakan k-Nearest Neighbor Dengan Ekstraksi Fitur HOG Yohannes Yohannes; Yulya Puspita Sari; Indah Feristyani
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 1 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i1.1584

Abstract

Mammal is a type of animal that has many diverse characteristics, such as vertebrates and breastfeeding. In this study, the HOG feature and the k-NN method were proposed to classify 15 species of mammals. This study uses the LHI-Animal-Faces dataset which has fifteen species of mammals, where each type of mammal has 50 images measuring 100x100 pixels. The image will be conducted the process by the HOG feature extraction process and continued into the classification process using k-Nearest Neighbor. The performance of the HOG and k-NN features that get the best value is in deer and monkey, the best results for precision, recall, and accuracy are at k=3 where HOG feature extraction provides good vector features to be used in the classification process using the k-NN method.
Co-Authors Yohannes Yohannes Yulya Puspita Sari