Implementation of Deep Learning Model for Classification of Household Trash Image

Authors

  • Robet Department of Informatics STMIK TIME, Medan, Indonesia
  • Johanes Terang Kita Perangin Angin Department of Information System STMIK TIME, Medan, Indonesia
  • Octara Pribadi Department of Informatics STMIK TIME, Medan, Indonesia

DOI:

10.33395/sinkron.v8i4.14198

Abstract

The problem of household waste management is a very important issue today, where the rapid urbanization, consumptive culture, and the tendency to dispose of waste without sorting it first from home, makes the volume of waste in landfills increase. Therefore, household waste management needs to be managed quickly and appropriately, so as not to have a major impact on environmental, hygiene, and health problems. Although some environmental communities and local governments have made efforts to manage waste through recycling systems, the long-term use of human labor is inefficient, expensive, and harmful to workers' health. Therefore, utilizing artificial intelligence technology is the best solution to classify waste types quickly and accurately. This research tries to test several pre-trained convolutional neural network (CNN) models to perform classification. The results of testing pre-trained CNN models, such as AlexNet, VGG16, VGG19, ResNet50, and ResNeXt50, found that the pre-trained model ResNext50 is better with 100% accuracy, while the training loss and validation loss are 0.0414 and 0.0304, respectively. Then the second best model is the pre-trained ResNet50 model with 100% accuracy with training loss and validation loss of 0.0832 and 0.1077, respectively.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Beardsley, D. (1985). The impact of recycling on the environment. Conservation and Recycling, 8(3–4), 387–391. https://doi.org/10.1016/0361-3658(85)90009-8

Chen, Y., Han, W., Jin, J., Wang, H., Xing, Q., & Zhang, Y. (2021). Clean Our City: An Automatic Urban Garbage Classification Algorithm Using Computer Vision and Transfer Learning Technologies. Journal of Physics: Conference Series, 1994(1). https://doi.org/10.1088/1742-6596/1994/1/012022

Hidayat Salam. (2023). Penanganan Sampah Dimulai dari Rumah Tangga. Kompas.Id. https://www.kompas.id/baca/humaniora/2023/01/31/penanganan-sampah-dimulai-dari-rumah-tangga

Hossen, M. M., Ashraf, A., Hasan, M., Majid, M. E., Nashbat, M., Kashem, S. B. A., Kunju, A. K. A., Khandakar, A., Mahmud, S., & Chowdhury, M. E. H. (2024). GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management. Waste Management, 174, 439–450. https://doi.org/10.1016/j.wasman.2023.12.014

Indraswari, D. L. (2023). Darurat Pengelolaan Sampah di Indonesia. Kompas.Com.

Kang, Z., Yang, J., Li, G., & Zhang, Z. (2020). An Automatic Garbage Classification System Based on Deep Learning. IEEE Access, 8, 140019–140029. https://doi.org/10.1109/ACCESS.2020.3010496

Kaya, M., Ulutürk, S., Kaya, Y. Ç., & Altıntaş, O. (2023). Optimization of Several Deep CNN Models . 6(2), 91– 104.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25 (NIPS 2012), 1–1432. https://doi.org/10.1201/9781420010749

Mao, W. L., Chen, W. C., Wang, C. T., & Lin, Y. H. (2021). Recycling waste classification using optimized convolutional neural network. Resources, Conservation and Recycling, 164(July 2020), 105132. https://doi.org/10.1016/j.resconrec.2020.105132

Pothineni, R. S., Inampudi, S., Gudavalli, L. Y., & Lakshmi Surekha, T. (2023). Traffic Sign Classification using Deep Learning. Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023, 527–531. https://doi.org/10.1109/ICAIS56108.2023.10073690

Pučnik, R., Dokl, M., Van Fan, Y., Vujanović, A., Novak Pintarič, Z., Aviso, K. B., Tan, R. R., Pahor, B., Kravanja, Z., & Čuček, L. (2024). A waste separation system based on sensor technology and deep learning: A simple approach applied to a case study of plastic packaging waste. Journal of Cleaner Production, 141762. https://doi.org/10.1016/j.jclepro.2024.141762

Rahman, M. W., Islam, R., Hasan, A., Bithi, N. I., Hasan, M. M., & Rahman, M. M. (2022). Intelligent waste management system using deep learning with IoT. Journal of King Saud University - Computer and Information Sciences, 34(5), 2072–2087. https://doi.org/10.1016/j.jksuci.2020.08.016

Robet, Juliandy, C., Andi, Hendri, Hendrik, J., & Tarigan, F. A. (2022). Image Road Surface Classification Based on GLCM Feature Using LGBM Classifier. IOP Conference Series: Earth and Environmental Science, 1083(1). https://doi.org/10.1088/1755-1315/1083/1/012006

Sethi, S., Kathuria, M., & Kaushik, T. (2021). Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread. Journal of Biomedical Informatics, 120. https://doi.org/10.1016/j.jbi.2021.103848

Shi, C., Tan, C., Wang, T., & Wang, L. (2021). A waste classification method based on a multilayer hybrid convolution neural network. Applied Sciences (Switzerland), 11(18). https://doi.org/10.3390/app11188572

sipsn.menlhk.go.id. (2023). Capaian Kinerja Pengelolaan Sampah. Sistem Informasi Pengelolaan Sampah Nasional Kementerian Lingkungan Hidup Dan Kehutanan

Thung, G. (2017). Dataset of images of trash. https://github.com/garythung/trashnet

Tiyajamorn, P., Lorprasertkul, P., Assabumrungrat, R., Poomarin, W., & Chancharoen, R. (2019). Automatic Trash Classification using Convolutional Neural Network Machine Learning. Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems and Robotics, Automation and Mechatronics, CIS and RAM 2019, 71–76. https://doi.org/10.1109/CIS-RAM47153.2019.9095775

Verma, V., Gupta, D., Gupta, S., Uppal, M., Anand, D., Ortega-Mansilla, A., Alharithi, F. S., Almotiri, J., & Goyal, N. (2022). A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle. Symmetry, 14(5). https://doi.org/10.3390/sym14050960

Yang, Z., Xia, Z., Yang, G., & Lv, Y. (2022). A Garbage Classification Method Based on a Small Convolution Neural Network. Sustainability (Switzerland), 14(22). https://doi.org/10.3390/su142214735

Zeiler, M. D. (2012). ADADELTA: An Adaptive Learning Rate Method. http://arxiv.org/abs/1212.5701

Downloads


Crossmark Updates

How to Cite

Robet, R., Perangin Angin, J. T. K. ., & Pribadi, O. . (2024). Implementation of Deep Learning Model for Classification of Household Trash Image . Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(4), 2575-2583. https://doi.org/10.33395/sinkron.v8i4.14198

Issue

Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024

Section

Articles

License

Copyright (c) 2024 Robet, Johanes Terang Kita Perangin Angin, Octara Pribadi

Creative Commons License

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

Crossref
Scopus
Google Scholar
Europe PMC