PERBANDINGAN ALGORITMA EFFICIENTNETB0 DAN INCEPTIONV3 DALAM KLASIFIKASI CITRA JENIS ANJING

Authors

  • Oriza Sativa Fiojati Universitas Gunadarma
  • Nani Mintarsih Universitas Gunadarma
  • Yuli Maharetta Arianti Universitas Gunadarma

DOI:

https://doi.org/10.56127/juit.v2i2.677

Keywords:

deep learning, efficientnetb0, inceptionv3, image classification

Abstract

Dogs are pets that have hundreds of breeds. The various breeds of dogs can be distinguished by size, fur color, and type of fur. The identification of dog breeds can use images of dogs as one of the object recognition information. Image classification can be made using Deep Learning methods. Image classification is the process of grouping pixels in an image into a number of classes, so that each class can represent an entity with certain characteristics. Additionally, Deep Learning has many algorithms that can be used for image classification, including the InceptionV3 and EfficientNetB0 algorithms. The purpose of this research is to compare the accuracy results of the EfficientNetB0 and InceptionV3 algorithms in classifying dog breed images. The stages in making this program include collecting datasets, data preparation, model design, and interpreting the results. This image classification is made using the Python programming language and Visual Studio Code software. The program has an accuracy result with the best model being InceptionV3, with an accuracy of 97.30% on the training set and 97.07% on the validation set after 20 epochs.

References

Andrew, N. H. (2009) “Hands-On Python A Tutorial Introduction for Beginners.” Available at : http://anh.cs.luIII.edu//python/hands on/handson.pdf

Chao, S. and Wenjun, W. (2020) ”Concrete Cracks Detection Using Convolutional Neural Network Based on Transfer Learning.” Hohai University, Nanjing 210098, China.

Cormen, T.H. (1989) “Introduction to Algorithms.” The MIT Press.

Danukusumo, K.P. (2017). “Implementasi Deep Learning

Menggunakan Convolutional Neural Network untuk Klasifikasi Citra Candi Berbasis GPU.”

Gonzales, R.C. Woods, R.E. (2002) “Digital Image Processing.” New Jersey : Prentice-Hall, Inc.

Lillesand, T.M. and Kiefer, R.W. (1994) “Penginderaan Jauh dan Interpretasi Citra.” Gadjah Mada University Press, Yogyakarta.

Mingxing, T. and Quoc, V. L. (2020) “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.” arXiv:1905.11946v5

Rania, R., Madhavan, A. and Ng, A.Y. (2009) “Large-Scale Deep.

Published

2023-05-30

How to Cite

Oriza Sativa Fiojati, Nani Mintarsih, & Yuli Maharetta Arianti. (2023). PERBANDINGAN ALGORITMA EFFICIENTNETB0 DAN INCEPTIONV3 DALAM KLASIFIKASI CITRA JENIS ANJING. Jurnal Ilmiah Teknik, 2(2), 12–16. https://doi.org/10.56127/juit.v2i2.677

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