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.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Fahmi, M., & Yudhana, A. (2023). Pemilahan Sampah Menggunakan Model Klasifikasi Support Vector Machine Gabungan dengan Convolutional Neural Network. Jurnal Riset Komputer), 10(1), 2407–389. https://doi.org/10.30865/jurikom.v10i1.5468

Fantara, F. P., Syauqy, D., & Setyawan, G. E. (2018). Implementasi Sistem Klasifikasi Sampah Organik dan Anorganik dengan Metode Jaringan Saraf Tiruan Backpropagation (Vol. 2, Issue 11). http://j-ptiik.ub.ac.id

Hurst, W., Ebo Bennin, K., Kotze, B., Mangara, T., Nnamoko, N., Barrowclough, J., & Procter, J. (2022). Solid Waste Image Classification Using Deep Convolutional Neural Network. https://doi.org/10.3390/infrastructures

Kurniawan, R., Wintoro, P. B., Mulyani, Y., & Komarudin, M. (2023). IMPLEMENTASI ARSITEKTUR XCEPTION PADA MODEL MACHINE LEARNING KLASIFIKASI SAMPAH ANORGANIK. Jurnal Informatika Dan Teknik Elektro Terapan, 11(2). https://doi.org/10.23960/jitet.v11i2.3034

Liao, T., Li, L., Ouyang, R., Lin, X., Lai, X., Cheng, G., & Ma, J. (2023). Classification of asymmetry in mammography via the DenseNet convolutional neural network. European Journal of Radiology Open, 11, 100502. https://doi.org/10.1016/j.ejro.2023.100502

Malik, M., Sharma, S., Uddin, M., Chen, C. L., Wu, C. M., Soni, P., & Chaudhary, S. (2022). Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models. Sustainability (Switzerland), 14(12). https://doi.org/10.3390/su14127222

Ode Rosnawati, W., Hasna Ahmad, D., Program Studi Pendidikan Biologi FKIP Universitas Khairun, M., & Program Studi Pendidikan Biologi Universitas Khairun, D. (n.d.). Pengelolaan Sampah Rumah Tangga Masyarakat Pemukiman Atas Laut Di Kecamatan Kota Ternate. http://ejournal.unkhair.ac.id/index.php/Techno

Pardede, J., & Putra, D. A. L. (2020). Implementasi DenseNet Untuk Mengidentifikasi Kanker Kulit Melanoma. Jurnal Teknik Informatika Dan Sistem Informasi, 6(3). https://doi.org/10.28932/jutisi.v6i3.2814

Prakash, N. N., Rajesh, V., Namakhwa, D. L., Dwarkanath Pande, S., & Ahammad, S. H. (2023). A DenseNet CNN-based liver lesion prediction and classification for future medical diagnosis. Scientific African, 20, e01629. https://doi.org/10.1016/j.sciaf.2023.e01629

Ramadhani, R. D., Thohari, A. N. A., Kartiko, C., Junaidi, A., & Laksana, T. G. (2021). Implementation of Deep Learning for Organic and Anorganic Waste Classification on Android Mobile.

Saputra, A. D., Hindarto, D., & Santoso, H. (2023). Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201. Sinkron, 8(1), 48–55. https://doi.org/10.33395/sinkron.v8i1.11906

Syamsir, S., & Pangestuty, D. M. (2020). Autocorrelation of Spatial Based Dengue Hemorrhagic Fever Cases in Air Putih Area, Samarinda City. JURNAL KESEHATAN LINGKUNGAN, 12(2), 78. https://doi.org/10.20473/jkl.v12i2.2020.78-86

Wong, J. (2022). BULLETIN OF COMPUTER SCIENCE RESEARCH Aplikasi Klasifikasi Sampah Organik dan Non Organik dengan Metode GLCM Dan LS-SVM. Media Online), 3(1). https://doi.org/10.47065/bulletincsr.v3i1.198

Downloads


Crossmark Updates

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