Pengembangan Algoritma Convolutional Neural Networks (CNN) untuk Klasifikasi Objek dalam Gambar Sampah
Abstract
Waste is a serious issue facing the world today, with increasing human activity and global economic growth. One important step in waste management is the classification process, which aims to separate types of waste based on their characteristics so they can be recycled, processed, or disposed of properly. Previous research has shown that Convolutional Neural Networks (CNN) are effective algorithms for multi-class classification. Therefore, this study develops an optimized CNN model for automatic waste classification, with a primary focus on improving accuracy through modifications to the CNN architecture. The dataset used consists of 17,366 waste images from various sources, which are then divided into training and testing data after undergoing preprocessing to ensure good data quality before training the model. However, one of the main challenges in developing a CNN model for multi-class classification is the risk of difficulty in learning class features, especially when the model is faced with too many classes. To address this issue, this study implements a strategy by adding convolutional layers to the CNN architecture. This method aims to deepen the network to capture more complex features from the given data, which in turn can improve the model's generalization to new data. Evaluation results show that the modified CNN model achieved a training accuracy of 88% after 40 epochs, with a testing accuracy of around 83%. This research not only contributes to the development of more advanced automatic waste classification technology but also provides a strong foundation for further research in this field. With increased waste management effectiveness, it is hoped to have a positive impact on the environment and public health as a whole..
Downloads
References
M. I. B. Ahmed et al., “Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management,” Sustainability (Switzerland), vol. 15, no. 14, 2023, doi: 10.3390/su151411138.
J. Li et al., “Automatic Detection and Classification System of Domestic Waste via Multimodel Cascaded Convolutional Neural Network,” IEEE Trans Industr Inform, vol. 18, no. 1, pp. 163–173, Jan. 2022, doi: 10.1109/TII.2021.3085669.
Q. Zhang, Q. Yang, X. Zhang, Q. Bao, J. Su, and X. Liu, “Waste image classification based on transfer learning and convolutional neural network,” Waste Management, vol. 135, pp. 150–157, Nov. 2021, doi: 10.1016/j.wasman.2021.08.038.
C. Shi, C. Tan, T. Wang, and L. Wang, “A waste classification method based on a multilayer hybrid convolution neural network,” Applied Sciences (Switzerland), vol. 11, no. 18, 2021, doi: 10.3390/app11188572.
M. Malik et al., “Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models,” Sustainability (Switzerland), vol. 14, no. 12, 2022, doi: 10.3390/su14127222.
N. Nnamoko, J. Barrowclough, and J. Procter, “Solid Waste Image Classification Using Deep Convolutional Neural Network,” Infrastructures (Basel), vol. 7, no. 4, 2022, doi: 10.3390/infrastructures7040047.
R. Sultana, R. D. Adams, Y. Yan, P. M. Yanik, and M. L. Tanaka, “Trash and Recycled Material Identification using Convolutional Neural Networks (CNN),” in Conference Proceedings - IEEE SOUTHEASTCON, Institute of Electrical and Electronics Engineers Inc., Mar. 2020. doi: 10.1109/SoutheastCon44009.2020.9249739.
S. Liang and Y. Gu, “A deep convolutional neural network to simultaneously localize and recognize waste types in images,” Waste Management, vol. 126, pp. 247–257, May 2021, doi: 10.1016/j.wasman.2021.03.017.
M. Fan, K. Zuo, J. Wang, and J. Zhu, “A lightweight multiscale convolutional neural network for garbage sorting,” Systems and Soft Computing, vol. 5, Dec. 2023, doi: 10.1016/j.sasc.2023.200059.
W. L. Mao, W. C. Chen, C. T. Wang, and Y. H. Lin, “Recycling waste classification using optimized convolutional neural network,” Resour Conserv Recycl, vol. 164, Jan. 2021, doi: 10.1016/j.resconrec.2020.105132.
M. A. Islam, S. I. Rashid, N. U. I. Hossain, R. Fleming, and A. Sokolov, “An integrated convolutional neural network and sorting algorithm for image classification for efficient flood disaster management,” Decision Analytics Journal, vol. 7, Jun. 2023, doi: 10.1016/j.dajour.2023.100225.
P. Nowakowski and T. Pamuła, “Application of deep learning object classifier to improve e-waste collection planning,” Waste Management, vol. 109, pp. 1–9, May 2020, doi: 10.1016/j.wasman.2020.04.041.
B. Akshaya and M. T. Kala, “Convolutional Neural Network Based Image Classification and New Class Detection,” in Proceedings of 2020 IEEE International Conference on Power, Instrumentation, Control and Computing, PICC 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/PICC51425.2020.9362375.
A. Altikat, A. Gulbe, and S. Altikat, “Intelligent solid waste classification using deep convolutional neural networks,” International Journal of Environmental Science and Technology, vol. 19, no. 3, 2022, doi: 10.1007/s13762-021-03179-4.
O. I. Funch, R. Marhaug, S. Kohtala, and M. Steinert, “Detecting glass and metal in consumer trash bags during waste collection using convolutional neural networks,” Waste Management, vol. 119, pp. 30–38, Jan. 2021, doi: 10.1016/j.wasman.2020.09.032.
X. Tian, L. Shi, Y. Luo, and X. Zhang, “Garbage Classification Algorithm Based on Improved MobileNetV3,” IEEE Access, vol. 12, pp. 44799–44807, 2024, doi: 10.1109/ACCESS.2024.3381533.
M. I. B. Ahmed et al., “Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management,” Sustainability (Switzerland), vol. 15, no. 14, Jul. 2023, doi: 10.3390/su151411138.
M. Chhabra, B. Sharan, M. Elbarachi, and M. Kumar, “Intelligent waste classification approach based on improved multi-layered convolutional neural network,” Multimed Tools Appl, 2024, doi: 10.1007/s11042-024-18939-w.
T. V. Janahiraman and P. Subramaniam, “Gender classification based on asian faces using deep learning,” in 2019 IEEE 9th International Conference on System Engineering and Technology, ICSET 2019 - Proceeding, 2019. doi: 10.1109/ICSEngT.2019.8906399.
S. Soim, “Development of Convolutional Neural Network Models to Improve Facial Expression Recognition Accuracy,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 10, no. 2, pp. 279–289, 2024, doi: 10.26555/jiteki.v10i2.28863.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Pengembangan Algoritma Convolutional Neural Networks (CNN) untuk Klasifikasi Objek dalam Gambar Sampah
Pages: 797-806
Copyright (c) 2024 Yoke Annisa Putri Vandalis, Sopian Soim, Lindawati Lindawati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).