KLASIFIKASI RAS ANJING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR VGG-16

Abstract

Dogs are lovable animals that bring companionship and fun to any household, but things to consider before getting a dog are their daily needs including food, shelter, and animal care, as well as affection and physical and mental stimulation. This research was conducted to introduce dog breeds and how to care for each breed. This system is built using Mask R-CNN and Convolutional Neural Network (CNN), a deep learning architecture.

Mask R-CNN model is used for detecting and cropping dog in images, trained using Microsoft Common Object of Context (MS COCO) dataset. CNN is used for classification of dog breeds in images and is trained using 17.513 images of 17 different breeds. Result for Mask R-CNN show the detection accuracy for dogs has 74% using test images, and CNN show the identification accuracy using test images has 82% accuracy, and for CNN using cropped images has 87% accuracy.