Classification of beetle type using the Convolutional Neural Network algorithm

Authors

  • Insidini Fawwaz Universitas Prima Indonesia, Indonesia
  • Tomy Candra Universitas Prima Indonesia
  • Delima Agustina Margareta Marpaung Universitas Prima Indonesia, Indonesia
  • Arun Dinis Universitas Prima Medan, Indonesia
  • M Reza Fachrozi Universitas Prima Indonesia, Indonesia

DOI:

10.33395/sinkron.v7i4.11673

Keywords:

Image Classification, Convolutional Neural Network, ResNet50, VGG16, Confusion Matrix, Bettle

Abstract

Beetles (Order Coleoptera) are the largest order of animals. Beetles are a group of insects that make up the order Coleoptera. Estimates of the total number of living beetle species are millions of beetle species whose features make it difficult to visually identify beetle species. Currently, the beetle classification process is still carried out using direct observation and personal assumptions. CNN model ResNet50 is one of the ResNet variants that has 50 layers and VGG16 is a CNN model that utilizes a convolutional layer with a small convolutional filter specification (3×3) and always uses the same padding and maxpool layers of a 2x2 filter. In this Algorithm (CNN) with the ResNet50 model, it succeeded in exploring beetles with accuracy, precision, recall and F-1 Score with values of 93%, 94.24%, 89.28%, 91.69%, while the VGG16 model succeeded in conducting research on beetle species with accuracy, precision, recall and F-1 Score with values of 86.9%, 87.5%, 87%, 87.2%, so it can be said that the classification of beetle species using the CNN algorithm with the ResNet50 model is better than the VGG16 model.

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How to Cite

Fawwaz, I. ., Candra, T., Marpaung, D. A. M. ., Dinis, A. ., & Fachrozi, M. R. . (2022). Classification of beetle type using the Convolutional Neural Network algorithm. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2340-2348. https://doi.org/10.33395/sinkron.v7i4.11673