PENERAPAN CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK DETEKSI RAS KUCING DAN ANJING DENGAN METODE MOBILENETV2

Roma Arrizal  Omar Syah; Achmad Nur Sholeh

Authors

Roma Arrizal Omar Syah STMIK Amikom Surakarta Author
Achmad Nur Sholeh Universitas Pamulang Author

Keywords:

Animal Breed, Convolutional Neural Network, Image Classification, MobileNetV2

Abstract

This research applies a Convolutional Neural Network (CNN) based on MobileNetV2 as an automatic system for efficient identification of cat and dog breeds without requiring special skills. This research is motivated by the need for an animal breed identification method that is fast, accurate, and easy to use by the general public, especially in pet data collection activities, adoption services, and veterinary clinics. The dataset used consists of 3,400 images with 17 classes, including 8 cat breeds and 9 dog breeds, which are divided into training and validation data with a ratio of 80:20. The model was built using the MobileNetV2 transfer learning approach with the addition of Global Average Pooling. The training process is carried out for a maximum of 30 epochs using an Early Stopping mechanism with three optimizers, namely Adam, RMSprop, and Stochastic Gradient Descent (SGD). The research results show that the Adam optimizer produces training accuracy of 95.66% and validation accuracy of 94.71% with stable loss, RMSprop achieves training accuracy of 95.51% and validation of 94.85%, while SGD obtains the highest training accuracy of 96.69% with validation accuracy of 95.00%. Based on these results, it can be concluded that the MobileNetV2-based CNN is effective for classifying cat and dog breeds to support real applications in everyday life.

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