Detection of Inorganic Waste Using Convolutional Neural Network Method

Authors

  • Achmad Riduan Universitas Bina Darma, Indonesia
  • Febriyanti Panjaitan Universitas Bina Darma, Indonesia
  • Syahril Rizal Universitas Bina Darma, Indonesia
  • Nurul Huda Universitas Bina Darma, Indonesia
  • Susan Dian Purnamasari Universitas Bina Darma, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v6i1.662

Keywords:

Waste, Image Classification, CNN, ResNet

Abstract

Waste, encompassing both domestic and industrial materials, presents a significant environmental challenge. Effectively managing waste requires accurate identification and classification. Convolutional Neural Networks (CNNs), particularly the Residual Network (ResNet) architecture, have shown promise in image classification tasks. This research aims to utilize ResNet to identify types of waste, contributing to more efficient waste management practices. The ResNet101 architecture, comprising 101 layers, is employed in this study for waste classification. The dataset consists of 2527 images categorized into six classes: Cardboard, Glass, Metal, Paper, Plastic, and Trash. The ResNet model is pre-trained, leveraging existing knowledge to enhance classification accuracy. The dataset is divided into training and testing sets to evaluate the model's performance. The testing results, evaluated using a Confusion Matrix, demonstrate strong performance in waste classification. The ResNet101 model achieves 92% accuracy in detecting inorganic waste objects within the training dataset and maintains a high accuracy of 90% on the testing dataset. This indicates the effectiveness of the ResNet architecture in accurately identifying various types of waste, contributing to improved waste management efforts. he utilization of ResNet101 for waste classification yields promising results, with high accuracy rates observed across both training and testing datasets. By effectively identifying types of waste, this approach facilitates more efficient waste management practices, enabling better resource allocation and environmental conservation. Further research and application of CNN architectures in waste management could lead to enhanced sustainability efforts and improved waste-handling strategies.

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References

R. D. Ramadhani, A. N. A. Thohari, C. Kartiko, A. Junaidi, T. G. Laksana, and N. A. S. Nugraha, “Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 312–318, 2021.

H. I. Abdel-Shafy and M. S. M. Mansour, “Solid waste issue: Sources, composition, disposal, recycling, and valorization,” Egypt. J. Pet., vol. 27, no. 4, pp. 1275–1290, 2018.

T. Noor et al., “Types, sources and management of urban wastes,” in Urban ecology, Elsevier, 2020, pp. 239–263.

P. Singh and V. P. Sharma, “Integrated plastic waste management: environmental and improved health approaches,” Procedia Environ. Sci., vol. 35, pp. 692–700, 2016.

M. Yang and G. Thung, “Classification of trash for recyclability status,” CS229 Proj. Rep., vol. 2016, no. 1, p. 3, 2016.

J. Nuariputri and P. Sukmasetya, “Klasifikasi Jenis Sampah Menggunakan Base ResNet-50,” J. Ilm. KOMPUTASI, vol. 22, no. 3, pp. 379–386, 2023.

S. Stephen, R. Raymond, and H. Santoso, “Applikasi Convolution Neural Network Untuk Mendeteksi Jenis-jenis Sampah,” Explor. J. Sist. Inf. dan Telemat. (Telekomunikasi, Multimed. dan Inform., vol. 10, no. 2, 2019.

P. N. Ma’rifah, M. Sarosa, and E. Rohadi, “Comparison of Faster R-CNN ResNet-50 and ResNet-101 Methods for Recycling Waste Detection.”

Z. M. A. Amin, K. N. Sami, and R. Hassan, “An approach of classifying waste using transfer learning method,” Int. J. Perceptive Cogn. Comput., vol. 7, no. 1, pp. 41–52, 2021.

S. Liu, J. Zhang, Y. Xiang, W. Zhou, and D. Xiang, “A study of data pre-processing techniques for imbalanced biomedical data classification,” Int. J. Bioinform. Res. Appl., vol. 16, no. 3, pp. 290–318, 2020.

F. Kamiran and T. Calders, “Data preprocessing techniques for classification without discrimination,” Knowl. Inf. Syst., vol. 33, no. 1, pp. 1–33, 2012.

F. F. Maulana and N. Rochmawati, “Klasifikasi Citra Buah Menggunakan Convolutional Neural Network,” J. Informatics Comput. Sci., vol. 1, no. 02, pp. 104–108, 2019.

Z. Xu, K. Sun, and J. Mao, “Research on ResNet101 network chemical reagent label image classification based on transfer learning,” in 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT, 2020, pp. 354–358.

M. A. Hanin, R. Patmasari, and R. Y. N. Fuâ, “Sistem Klasifikasi Penyakit Kulit Menggunakan Convolutional Neural Network (cnn),” eProceedings Eng., vol. 8, no. 1, 2021.

Q. Zhang, “A novel ResNet101 model based on dense dilated convolution for image classification,” SN Appl. Sci., vol. 4, pp. 1–13, 2022.

S. Vaishali and S. Neetu, “Enhanced copy-move forgery detection using deep convolutional neural network (DCNN) employing the ResNet-101 transfer learning model,” Multimed. Tools Appl., vol. 83, no. 4, pp. 10839–10863, 2024.

R. G. Guntara, “Deteksi Atap Bangunan Berbasis Citra Udara Menggunakan Google Colab dan Algoritma Deep Learning YOLOv7,” J. Manaj. Sist. Inf., vol. 2, no. 1, pp. 9–18, 2023.

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Published

2024-03-24

Issue

Vol. 6 No. 1 (2024): March

Section

Articles

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  1. The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
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How to Cite

[1]
A. Riduan, F. Panjaitan, S. Rizal, N. Huda, and S. D. Purnamasari, “Detection of Inorganic Waste Using Convolutional Neural Network Method”, journalisi, vol. 6, no. 1, pp. 290–300, Mar. 2024, doi: 10.51519/journalisi.v6i1.662.

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