Waste Classification Model Optimization with Modified MobileNetV3 for Efficient Waste Management

Putri Andani, Ramadian Ridho Illahi, I Wayan Sudiarta, Marzuki Marzuki, Arif Budianto

Abstract


The increase in population and economic activity has a significant impact on the amount of waste. Data in 2023 states that waste in Indonesia still cannot be managed properly.  One solution to overcome this problem is through recycling with waste sorting as a crucial stage. This research develops a waste classification model using modified MobileNetV3S. The classification process is performed using Convolutional Neural Network (CNN) method and parameter fine-tuning. This model is able to classify five different categories of waste, namely plastic bottles, leaves, plastic sheets, paper, and metal. The results show that the validation accuracy reaches 96.2% with a loss value of 0.049. These results can significantly contribute to better and sustainable waste management efforts.

Keywords


Waste Management; Classification; MobileNetV3S; Fine-Tuning

Full Text:

PDF

References


A. Z. Saputra and Ah. S. Fauzi, “Pengolahan Sampah Kertas Menjadi Bahan Baku Industri Kertas Bisa Mengurangi Sampah di Indonesia,” Jurnal Mesin Nusantara, vol. 5, no. 1, pp. 41–52, Jun. 2022, doi: 10.29407/jmn.v5i1.17522.

N. E. Lestari, A. Purnama, A. Safitri, and Y. Koto, “Peningkatan Pengetahuan dan Sikap Pemilahan Sampah Pada Anak Usia Sekolah Melalui Metode Simulasi,” Jurnal Pengabdian Masyarakat Indonesia Maju, vol. 1, no. 02, pp. 45–49, Sep. 2020, doi: 10.33221/jpmim.v1i02.668.

R. Fermat Silolongan, T. Apriyono, A. Program Studi Ekonomi Pembangunan, S. Jambatan Bulan, and D. Program Studi Ekonomi Pembangunan, “ANALISIS FAKTOR PENGHAMBAT EFEKTIVITAS PENGELOLAAN SAMPAH DI KABUPATEN MIMIKA.”

A. R. Javed, W. Ahmed, S. Pandya, P. K. R. Maddikunta, M. Alazab, and T. R. Gadekallu, “A Survey of Explainable Artificial Intelligence for Smart Cities,” Electronics (Switzerland), vol. 12, no. 4. MDPI, Feb. 01, 2023. doi: 10.3390/electronics12041020.

D. Szpilko, A. de la Torre Gallegos, F. Jimenez Naharro, A. Rzepka, and A. Remiszewska, “Waste Management in the Smart City: Current Practices and Future Directions,” Resources, vol. 12, no. 10. Multidisciplinary Digital Publishing Institute (MDPI), Oct. 01, 2023. doi: 10.3390/resources12100115.

R. Chauhan, S. Shighra, H. Madkhali, L. Nguyen, and M. Prasad, “Efficient Future Waste Management: A Learning-Based Approach with Deep Neural Networks for Smart System (LADS),” Applied Sciences (Switzerland), vol. 13, no. 7, Apr. 2023, doi: 10.3390/app13074140.

Leonardo, Yohannes, and E. Hartati, “Klasifikasi Sampah Daur Ulang Menggunakan Support Vector Machine DENGAN Fitur Local Binary Pattern,” Jurnal Algoritme, vol. 1, no. 1, pp. 78–89, 2020.

J. Gu, J. Zhang, H. Sui, and B. Zou, “Research on Intelligent Trash Bin Design Based on Visual Recognition of Machine,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Mar. 2021. doi: 10.1088/1755-1315/687/1/012170.

“Klasifikasi Jenis Sampah Menggunakan Base ResNet-50,” Jurnal Ilmiah Komputasi, vol. 22, no. 3, Oct. 2023, doi: 10.32409/jikstik.22.3.3380.

V. Kaya, “Classification of waste materials with a smart garbage system for sustainable development: a novel model,” Front Environ Sci, vol. 11, 2023, doi: 10.3389/fenvs.2023.1228732.

S. Qian, Y. Hu, and C. Ning, “MobileNetV3 for Image Classification,” in International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE 2021), 2021, pp. 490–497.

V. Pichiyan, S. Muthulingam, G. Sathar, S. Nalajala, A. Ch, and M. N. Das, “Web Scraping using Natural Language Processing: Exploiting Unstructured Text for Data Extraction and Analysis,” in Procedia Computer Science, Elsevier B.V., 2023, pp. 193–202. doi: 10.1016/j.procs.2023.12.074.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning.

X. Hu, W. Liu, J. Bian, and J. Pei, “Measuring Model Complexity of Neural Networks with Curve Activation Functions,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Aug. 2020, pp. 1521–1531. doi: 10.1145/3394486.3403203.




DOI: http://dx.doi.org/10.12962%2Fj24604682.v21i2.20876

Refbacks

  • There are currently no refbacks.


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