Densenet Architecture Implementation for Organic and Non-Organic Waste

Authors

  • Allwin M. Simarmata Indonesian Prima University,Cover Road, West White Sei,Medan 20118, Indonesia
  • Philander Salim Indonesian Prima University,Cover Road, West White Sei,Medan 20118, Indonesia
  • Netral Jaya Waruwu Indonesian Prima University,Cover Road, West White Sei,Medan 20118, Indonesia
  • Jessica Indonesian Prima University,Cover Road, West White Sei,Medan 20118, Indonesia

DOI:

10.33395/sinkron.v8i4.12765

Keywords:

Computer Vision, Convolutional Neural Network, DenseNet, Image Classification, Waste, Organic and Non Organic Waste.

Abstract

Garbage is the result left over from the process of daily human activities and activities which are considered no longer suitable for use, ranging from household waste to large-scale industrial waste. Therefore, the classification of waste is important because the problem of waste disposal is increasing and the way of processing is wrong. This research focuses on the classification of organic and non-organic waste using the DenseNet architecture. The dataset is processed first and each image in the dataset is resized to 128x128 pixels before being used in the model. We then trained all DenseNet types namely DenseNet121, DenseNet169, DenseNet 201, and compared their performance. Based on the test results, all DenseNet models that were trained were able to produce good accuracy, precision, recall, and F1 scores in garbage classification. In particular, our designed DenseNet121 model achieves 93.1 accuracy, 94.08% precision, 94.00% recall, 94.03% F1 score and 1min 34s training time as the best among other models. These results prove that the DenseNet architecture can be used to classify organic and non-organic waste correctly.

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How to Cite

Simarmata, A. M., Salim, P., Waruwu, N. J., & Jessica. (2023). Densenet Architecture Implementation for Organic and Non-Organic Waste. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2444-2449. https://doi.org/10.33395/sinkron.v8i4.12765

Issue

Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023

Section

Articles

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Copyright (c) 2023 Allwin Simarmata, Philander Salim, Netral Jaya Waruwu, Jessica

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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