Analisis Perbandingan Optimizer pada Arsitektur NASNetMobile Convolutional Neural Network untuk Klasifikasi Ras Kucing
DOI:
https://doi.org/10.34151/jurtek.v15i2.4025Keywords:
cat breed classification, convolutional neural network, deep learning, NASNetMobile, optimizerAbstract
Searching for research titles and abstracts is made easy with these keywords. Artificial Intelligence (AI) technology is currently developing very rapidly, there are various applications of AI that we can find in everyday life around us without realizing it. AI technology now allows us to work with computers more easily, just as we can know the type of cat breed and other information. There is deep learning that works by imitating the human brain or artificial neural networks to enhance current machine-learning capabilities. Deep learning can recognize and classify image categories. This study aims to determine the optimal optimizer in the classification of cat breeds. With the classification of cat breeds, cat keepers can find out the type of cat breed so they can find out how to care for it, the activities, and the personality possessed by the cat. The use of algorithm method used in this study uses the CNN algorithm with the NASNetMobile architecture. The dataset contains 840 images which are divided into 4 classes and divided into 588 training data, 168 testing data, and 84 validation data. for the RMSprop optimizer with a learning rate of 0.0001 to get an accuracy of 89.88%, this result is the highest among the others. Meanwhile, the SGD optimizer gets an accuracy of 78.57 & this result is the lowest. So it can be concluded that the architecture and optimizer are very important and influential in improving the performance of the model.
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Copyright (c) 2022 D. Diffran Nur Cahyo, Muhammad Anwar Fauzi, Jangkung Tri Nugroho, Kusrini
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