Analisa Komparasi Algoritma Machine Learning dan Deep Learning Dalam Klasifikasi Citra Ras Kucing

Royan Fajar Sultoni; Achmad Junaidi; Eva Yulia Puspaningrum

doi:10.61132/neptunus.v2i3.251

Authors

Royan Fajar Sultoni UPN "Veteran" Jawa Timur
Achmad Junaidi UPN "Veteran" Jawa Timur
Eva Yulia Puspaningrum UPN "Veteran" Jawa Timur

DOI:

https://doi.org/10.61132/neptunus.v2i3.251

Keywords:

Algorithm Comparison, Machine Learning, Deep Learning, SVM, KNN, CNN

Abstract

Cats (Felis catus) are a type of carnivorous mammal from the Felidae family that was domesticated and has been one of the animals that has mingled with humans since time immemorial. Domestic cats are broadly divided into 2 types, namely village cats and purebred cats. Purebred cats have quite a varied number of types. Therefore, confusion often occurs in determining the type or breed of cat. Meanwhile, in practice, each race does not have the same treatment (especially in the aspect of care). In digital image processing, Machine Learning and Deep Learning are the main aspects in the process of applying technology that can overcome this problem, so research related to this problem was designed. This research was conducted to add insight for further research in a more sophisticated and effective image recognition process. In the experiments carried out in this research, the SVM, KNN, and CNN methods were tested with the Xception and EfficientNet-B1 architectures. Based on the final results obtained from this test, the CNN method with the Xception architecture is the best model. By using fine-tuning and a learning-rate of 1e-5, this method produces a micro average value of 0.974, on a cat breed image dataset of 13 classes and 7800 images. Meanwhile, the method that produces the fastest ETA Training and Testing is obtained by the KNN method, with an ETA Training time of 0.194 seconds, and an ETA Testing time of 1.782 seconds.

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