Analisis Perbandingan Optimizer pada Arsitektur NASNetMobile Convolutional Neural Network untuk Klasifikasi Ras Kucing


  • D. Diffran Nur Cahyo Magister Teknik Informatika, Universitas Amikom Yogyakarta
  • Muhammad Anwar Fauzi Magister Teknik Informatika, Universitas Amikom Yogyakarta
  • Jangkung Tri Nugroho Magister Teknik Informatika, Universitas Amikom Yogyakarta
  • Kusrini Kusrini Magister Teknik Informatika, Universitas Amikom Yogyakarta



cat breed classification, convolutional neural network, deep learning, NASNetMobile, optimizer


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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P. A., Mapayi, T., & Tu, C. (2022). Intelligent Mobile Plant Disease Diagnostic System Using NASNet-Mobile Deep Learning. IAENG International Journal of Computer Science, 49(1), 216–231.

Amiruddin, B. P., & Kadir, R. E. A. (2020). CNN Architectures Performance Evaluation for Image Classification of Mosquito in Indonesia. Proceedings - 2020 International Seminar on Intelligent Technology and Its Application: Humanification of Reliable Intelligent Systems, ISITIA 2020, 223–227.

Bera, S., & Shrivastava, V. K. (2020). Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification. International Journal of Remote Sensing, 41(7), 2664–2683.

Crowell-Davis, S. L., Curtis, T. M., & Knowles, R. J. (2004). Social organization in the cat: A modern understanding. Journal of Feline Medicine and Surgery, 6(1), 19–28.

Imanuel, A., & Setiabudi, D. H. (n.d.). Penerapan Convolutional Neural Network dengan Pre-Trained Model Xception untuk Meningkatkan Akurasi dalam Mengidentifikasi Jenis Ras Kucing.

Jaka, K., Abwabul, J., Muhammad Zulkarnain, L., Rubianto, & Rika, R. (2022). Komparasi Algoritma Support Vector Machine Dan Naive Bayes Pada Klasifikasi Ras Kucing. Jurnal Generic, 14(1), 8–12.

Karlita, T., Choirunisa, N. A., Asmara, R., & Setyorini, F. (2022). Cat Breeds Classification Using Compound Model Scaling Convolutional Neural Networks. Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (ICAST-SS 2021), 647, 909–914.

Lipinski, M. J., Froenicke, L., Baysac, K. C., Billings, N. C., Leutenegger, C. M., Levy, A. M., Longeri, M., Niini, T., Ozpinar, H., Slater, M. R., Pedersen, N. C., & Lyons, L. A. (2008). The ascent of cat breeds: Genetic evaluations of breeds and worldwide random-bred populations. Genomics, 91(1), 12–21.

Menotti-Raymond, M. A., & O’Brien, S. (1995). Evolutionary conservation of ten microsatellite loci in four species of felidae. Journal of Heredity, 86(4), 319–322.

O’Brien, S. J., Johnson, W., Driscoll, C., Pontius, J., Pecon-Slattery, J., & Menotti-Raymond, M. (2008). State of cat genomics. Trends in Genetics, 24(6), 268–279.

Purnama, I. N. (2020). Herbal Plant Detection Based on Leaves Image Using Convolutional Neural Network With Mobile Net Architecture. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 6(1), 27–32.

Radhika, K., Devika, K., Aswathi, T., Sreevidya, P., Sowmya, V., & Soman, K. P. (2020). Performance analysis of NASNet on unconstrained ear recognition. Studies in Computational Intelligence, SCI 871, 57–82.

Vani, S., & Rao, T. V. M. (2019). An experimental approach towards the performance assessment of various optimizers on convolutional neural network. Proceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019, Icoei, 331–336.

Wikarta, A., Sigit Pramono, A., & Ariatedja, J. B. (2020). Analisa Bermacam Optimizer Pada Convolutional Neural Network Untuk Deteksi Pemakaian Masker Pengemudi Kendaraan. Seminar Nasional Informatika, 2020(Semnasif), 69–72.

Zhang, Q., Zhang, M., Chen, T., Sun, Z., Ma, Y., & Yu, B. (2019). Recent advances in convolutional neural network acceleration. Neurocomputing, 323, 37–51.



How to Cite

Nur Cahyo, D. D. ., Anwar Fauzi, M. ., Tri Nugroho, J. ., & Kusrini, K. (2022). Analisis Perbandingan Optimizer pada Arsitektur NASNetMobile Convolutional Neural Network untuk Klasifikasi Ras Kucing. Jurnal Teknologi, 15(2), 171–177.