DOI:
https://doi.org/10.59247/sjer.v1i1.12Keywords:
Morphology, HSV Color Model, CNN, Waste Management, Machine Learning, Image ProcessingAbstract
Waste management is essential in preserving nature to be cleaner and more well-maintained. Waste management runs slower than the speed of waste accumulation. One reason is slow waste sorting. This problem can be overcome by building a learning machine that can sort the types of waste. The type of waste often separated in the first sorting is waste based on its type, namely organic and inorganic. The classification model used is the CNN with image processing Morph-HSV color model. The data obtained from Kaggle is collected and processed using Python. The processed image is trained using a CNN classification model. The results of this study are an accuracy of 99.58% and a loss of 1.57%. With this research, it is hoped that it can accelerate waste sorting performance using the most efficient ML based on image processing and its classification model.
References
[1] A. Kahfi, “Tinjauan Terhadap Pengelolaan Sampah,” Jurisprud. Jur. Ilmu Huk. Fak. Syariah dan Huk., vol. 4, no. 1, p. 12, 2017, doi: 10.24252/jurisprudentie.v4i1.3661.
[2] T. Anh Khoa et al., “Waste Management System Using IoT-Based Machine Learning in University,” Wirel. Commun. Mob. Comput., vol. 2020, 2020, doi: 10.1155/2020/6138637.
[3] M. S. Ayilara, O. S. Olanrewaju, O. O. Babalola, and O. Odeyemi, “Waste management through composting: Challenges and potentials,” Sustain., vol. 12, no. 11, pp. 1–23, 2020, doi: 10.3390/su12114456.
[4] S. Dubey, P. Singh, P. Yadav, and K. K. Singh, “Household Waste Management System Using IoT and Machine Learning,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 1950–1959, 2020, doi: 10.1016/j.procs.2020.03.222.
[5] Z. Zainal, R. R. Rambey, and K. Rahman, “Governance of Household Waste Management in Pekanbaru City,” Mimb. J. Sos. dan Pembang., vol. 37, no. 2, pp. 275–285, 2021, doi: 10.29313/mimbar.v37i2.7947.
[6] H. nan Guo, S. biao Wu, Y. jie Tian, J. Zhang, and H. tao Liu, “Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review,” Bioresour. Technol., vol. 319, no. September 2020, p. 124114, 2021, doi: 10.1016/j.biortech.2020.124114.
[7] R. Setiadi, M. Nurhadi, and F. Prihantoro, “Idealisme dan Dualisme Daur Ulang Sampah di Indonesia: Studi Kasus Kota Semarang,” J. Ilmu Lingkung., vol. 18, no. 1, pp. 48–57, 2020, doi: 10.14710/jil.18.1.48-57.
[8] D. Gyawali, A. Regmi, A. Shakya, A. Gautam, and S. Shrestha, “Comparative Analysis of Multiple Deep CNN Models for Waste Classification,” 2020, [Online]. Available: http://arxiv.org/abs/2004.02168
[9] A. Semma, S. Lazrak, Y. Hannad, M. Boukhani, and Y. El Kettani, “WRITER IDENTIFICATION: The EFFECT of IMAGE RESIZING on CNN PERFORMANCE,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 46, no. 4/W5-2021, pp. 501–507, 2021, doi: 10.5194/isprs-Archives-XLVI-4-W5-2021-501-2021.
[10] M. Hashemi, “Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0263-7.
[11] A. Pavelka and A. Procházka, “Algorithms for initialization of neural network weights,” Sb. Prisp. 12. rocniku Konf. MATLAB 2004, vol. 2, pp. 453–459, 2004.
[12] K. Kangune, V. Kulkarni, and P. Kosamkar, “Grapes Ripeness Estimation using Convolutional Neural network and Support Vector Machine,” 2019 Glob. Conf. Adv. Technol. GCAT 2019, no. March, 2019, doi: 10.1109/GCAT47503.2019.8978341.
[13] J. Hu and B. Zhang, “Application Research of Automatic Garbage Sorting Based on TensorFlow and OpenCV,” J. Phys. Conf. Ser., vol. 1883, no. 1, pp. 1–6, 2021, doi: 10.1088/1742-6596/1883/1/012169.
[14] P. Ganesh, K. Volle, T. F. Burks, and S. S. Mehta, “Deep Orange: Mask R-CNN based Orange Detection and Segmentation,” IFAC-PapersOnLine, vol. 52, no. 30, pp. 70–75, 2019, doi: 10.1016/j.ifacol.2019.12.499.
[15] H. Min, X. Zhu, B. Yan, and Y. Yu, “Research on Visual Algorithm of Road Garbage Based on Intelligent Control of Road Sweeper,” J. Phys. Conf. Ser., vol. 1302, no. 3, 2019, doi: 10.1088/1742-6596/1302/3/032024.
[16] D. Hirafuji Neiva and C. Zanchettin, “Gesture recognition: A review focusing on sign language in a mobile context,” Expert Syst. Appl., vol. 103, pp. 159–183, 2018, doi: 10.1016/j.eswa.2018.01.051.
[17] H. M. Bui, M. Lech, E. Cheng, K. Neville, and I. S. Burnett, “Using grayscale images for object recognition with convolutional-recursive neural network,” 2016 IEEE 6th Int. Conf. Commun. Electron. IEEE ICCE 2016, pp. 321–325, 2016, doi: 10.1109/CCE.2016.7562656.
[18] H. J. Jie and P. Wanda, “Runpool: A dynamic pooling layer for convolution neural network,” Int. J. Comput. Intell. Syst., vol. 13, no. 1, pp. 66–76, 2020, doi: 10.2991/ijcis.d.200120.002.
[19] S. N. Aslan, R. Özalp, A. Uçar, and C. Güzeliş, “New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration,” Cluster Comput., vol. 25, no. 3, pp. 1575–1590, 2022, doi: 10.1007/s10586-021-03348-7.
[20] W. R. U. Fadilah, W. A. Kusuma, A. E. Minarno, and Y. Munarko, “Classification of Human Activity Recognition Utilizing Smartphone Data of CNN-LSTM,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 2020, 2021, doi: 10.22219/kinetik.v6i2.1319.
[21] K. Padmavathi and K. Thangadurai, “Implementation of RGB and grayscale images in plant leaves disease detection - Comparative study,” Indian J. Sci. Technol., vol. 9, no. 6, pp. 4–9, 2016, doi: 10.17485/ijst/2016/v9i6/77739.
[22] A. Yudhana, S. A. Akbar, A. Farezi, K. H. Ghazali, F. Nuraisyah, and P. A. Rosyady, “Glucose Content Analysis using Image Processing and Machine Learning Techniques,” in 2022 5th International Conference on Information and Communications Technology (ICOIACT), 2022, pp. 513–516. doi: 10.1109/ICOIACT55506.2022.9972142.
[23] A. S. Musliman, A. Fadlil, and A. Yudhana, “Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction,” J. Online Inform., vol. 6, no. 1, p. 63, 2021, doi: 10.15575/join.v6i1.704.
[24] A. Susanto, Z. H. Dewantoro, C. A. Sari, D. R. I. M. Setiadi, E. H. Rachmawanto, and I. U. W. Mulyono, “Shallot Quality Classification using HSV Color Models and Size Identification based on Naive Bayes Classifier,” J. Phys. Conf. Ser., vol. 1577, no. 1, 2020, doi: 10.1088/1742-6596/1577/1/012020.
[25] S. Sekar, “Waste Classification data,” 2019. https://www.kaggle.com/datasets/techsash/waste-classification-data (accessed Mar. 21, 2022).
[26] N. Yadav, S. M. Alfayeed, A. Khamparia, B. Pandey, D. N. H. Thanh, and S. Pande, “HSV model-based segmentation driven facial acne detection using deep learning,” Expert Syst., vol. 39, no. 3, pp. 1–13, 2022, doi: 10.1111/exsy.12760.
[27] G. Bargshady, X. Zhou, R. C. Deo, J. Soar, F. Whittaker, and H. Wang, “The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space,” Appl. Soft Comput. J., vol. 97, p. 106805, 2020, doi: 10.1016/j.asoc.2020.106805.
[28] T. B. D. G. Lafayette, L. F. Colaco, J. M. X. N. Teixeira, C. R. De Vasconcelos, and A. E. F. Da Gama, “Comparison of RGB and HSV color spaces for motion capture and analysis of individuals with limb discrepancy,” Proc. - 2019 21st Symp. Virtual Augment. Reality, SVR 2019, pp. 178–185, 2019, doi: 10.1109/SVR.2019.00042.
[29] J. Gola et al., “Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels,” Comput. Mater. Sci., vol. 160, no. October 2018, pp. 186–196, 2019, doi: 10.1016/j.commatsci.2019.01.006.
[30] P. K. Soni, N. Rajpal, and R. Mehta, “Semiautomatic Road Extraction Framework Based on Shape Features and LS-SVM from High-Resolution Images,” J. Indian Soc. Remote Sens., vol. 48, no. 3, pp. 513–524, 2020, doi: 10.1007/s12524-019-01077-4.
[31] A. Peryanto, A. Yudhana, and R. Umar, “Convolutional Neural Network and Support Vector Machine in Classification of Flower Images,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 8, no. 1, pp. 1–7, 2022, doi: 10.23917/khif.v8i1.15531.
[32] M. G Alaslani and L. A. Elrefaei, “Convolutional Neural Network Based Feature Extraction for IRIS Recognition,” Int. J. Comput. Sci. Inf. Technol., vol. 10, no. 2, pp. 65–78, 2018, doi: 10.5121/ijcsit.2018.10206.
[33] S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, “1D convolutional neural networks and applications: A survey,” Mech. Syst. Signal Process., vol. 151, p. 107398, 2021, doi: 10.1016/j.ymssp.2020.107398.
[34] Y. Chu, C. Huang, X. Xie, B. Tan, S. Kamal, and X. Xiong, “Multilayer hybrid deep-learning method for waste classification and recycling,” Comput. Intell. Neurosci., vol. 2018, 2018, doi: 10.1155/2018/5060857.
[35] C. F. G. Dos Santos and J. P. Papa, “Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks,” ACM Comput. Surv., vol. 54, no. 10s, pp. 1–25, 2022, doi: 10.1145/3510413.
[36] A. P. Puspaningrum et al., “Waste Classification Using Support Vector Machine with SIFT-PCA Feature Extraction,” ICICoS 2020 - Proceeding 4th Int. Conf. Informatics Comput. Sci., pp. 4–9, 2020, doi: 10.1109/ICICoS51170.2020.9298982.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Miftahuddin Fahmi, Anton Yudhana, Sunardi, Abdel-Nasser Sharkawy, Furizal
This work is licensed under a Creative Commons Attribution 4.0 International License.
