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All Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Yohannes
Universitas Multi Data Palemban
Computer Science & IT

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KLASIFIKASI MAMALIA MENGGUNAKAN EXTREME GRADIENT BOOSTING BERDASARKAN FITUR HISTOGRAM OF ORIENTED GRADIENT Yohannes; Johannes Petrus
BETRIK Vol. 13 No. 03 (2022): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/e2t7t733

Abstract

Mammals are one type of animal that has many characteristics and characteristics.The shape of the face in each type of mammal has a similar shape. The faces of mammals in theform of frontal images are a challenge in image classification. In this study, the Histogram ofOriented Gradient (HOG) is used as a feature of the facial shape of mammals. HOG is used as astrengthening feature in the classification process using the eXtreme Gradient Boosting(XGBoost) method. The test was carried out using a dataset of frontal facial imagery ofmammals consisting of 15 species. The results of the tests show that the XGBoost method with theHOG feature is able to provide better classification results for mammals than without the HOGfeature. This is indicated by an increase in the precision value of 0.61; recall of 0.62; and an f1-score of 0.60 on XGBoost with HOG feature which is almost double that of XGBoost withoutHOG feature.
Co-Authors Johannes Petrus