Leveraging Convolutional Neural Networks for Multiclass Waste Classification
DOI:
https://doi.org/10.30871/jaic.v9i4.9373Keywords:
Machine Learning, Convolutional Neural Networks, MobileNetV2, Multi-class Classification, WasteAbstract
The impact of population growth on waste production in Indonesia emphasizes the urgent need for effective waste management to mitigate environmental and health risks. Segregating waste into organic and inorganic categories is essential for sustainable management, enabling processes like composting and recycling. Employing convolutional neural networks (CNN) through machine learning presents a promising solution for waste classification. This study utilizes a CNN algorithm to achieve significant accuracy and precision in multi-class waste classification, with particular attention to areas for improvement, such as cardboard classification. Based on the MobileNetV2 architecture and Adam optimizer, the model demonstrates high accuracy and precision, with training and validation accuracy of 95.28% and 89.48%, respectively. High precision and recall values confirm its accurate waste classification. The evaluation of unseen data maintains an accuracy of 86.36%, indicating its generalization ability. However, variations in accuracy among waste classes suggest opportunities for refinement, particularly in cardboard classification.
Downloads
References
[1] “Sustainable cities & communities | SDG 11: Sustainable cities & communities.” Accessed: Apr. 18, 2025. [Online]. Available: https://datatopics.worldbank.org/sdgatlas/goal-11-sustainable-cities-and-communities/?lang=en
[2] “SIPSN - Sistem Informasi Pengelolaan Sampah Nasional.” Accessed: Apr. 20, 2025. [Online]. Available: https://sipsn.menlhk.go.id/sipsn/
[3] S. D. Meyrena and R. Amelia, “Analisis Pendayagunaan Limbah Plastik Menjadi Ecopaving Sebagai Upaya Pengurangan Sampah,” Indonesian Journal of Conservation, vol. 9, no. 2, pp. 96–100, Dec. 2020, doi: 10.15294/ijc.v9i2.27549.
[4] K. K. A. Sholihah, “Kajian Tentang Pengelolaan Sampah di Indonesia,” Swara Bhumi, vol. 03, no. 03, 2020.
[5] K. S. Nindya Ovitasari, D. Cantrika, Y. A. Murti, E. S. Widana, and I. G. A. Kurniawan, “Edukasi Pengolahan Sampah Organik dan Anorganik di Desa Rejasa Tabanan,” Bubungan Tinggi: Jurnal Pengabdian Masyarakat, vol. 4, no. 2, 2022, doi: 10.20527/btjpm.v4i2.4986.
[6] A. Angdresey, R. Mandala, and L. Wikarsa, “Application of Kolintang Traditional Music Instrument Using Motion Sensor Detection: Webcam,” in 2019 Fourth International Conference on Informatics and Computing (ICIC), 2019, pp. 1–5. doi: 10.1109/ICIC47613.2019.8985798.
[7] I. V. Masala and A. Angdresey, “The Real-Time Training System with Kinect: Trainer Approach,” in 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), 2017, pp. 233–237. doi: 10.1109/ICSIIT.2017.34.
[8] E. Alpaydin, Introduction to machine learning. MIT press, 2020.
[9] R. Ahn, S. Supakkul, L. Zhao, K. Kolluri, T. Hill, and L. Chung, “A Goal-Oriented Approach for Preparing a Machine-Learning Dataset to Support Business Problem Validation,” IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), AB, Canada, 2021.
[10] K. Hasan Mahmud and S. Al Faraby, “Klasifikasi Citra Multi-Kelas Menggunakan Convolutional Neural Network,” Bandung, 2019.
[11] D. Bhatt et al., “Cnn variants for computer vision: History, architecture, application, challenges and future scope,” Oct. 01, 2021, MDPI. doi: 10.3390/electronics10202470.
[12] C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electronic Markets, vol. 31, no. 3, pp. 685–695, 2021, doi: 10.1007/s12525-021-00475-2.
[13] A. Angdresey, L. Sitanayah, and E. Pantas, “Comparison of the Convolutional Neural Network Architectures for Traffic Object Classification,” in 2023 International Conference on Computer, Control, Informatics and its Applications (IC3INA), 2023, pp. 60–65. doi: 10.1109/IC3INA60834.2023.10285791.
[14] A. Pandey, A. Pandey, K. Maharjan, K. Shrestha, and J. Mansur, “Enhancing Waste Management: Automated Classification of Biodegradable and Non-biodegradable Waste using CNN,” in International Conference on Technologies for Computer, Electrical, Electronics & Communication (ICT-CEEL 2023), 2023, p. 1. [Online]. Available: https://www.researchgate.net/publication/376028733
[15] M. Grandini, E. Bagli, and G. Visani, “Metrics for Multi-Class Classification: An Overview,” arXiv:2008.05756, 2020.
[16] B. Pang, E. Nijkamp, and Y. N. Wu, “Deep Learning with TensorFlow: A Review,” 2020. doi: 10.3102/1076998619872761.
[17] K. Filus and J. Domańska, “Software vulnerabilities in TensorFlow-based deep learning applications,” Comput Secur, vol. 124, 2023, doi: 10.1016/j.cose.2022.102948.
[18] L. Yusuf and T. Hidayatulloh, “Implementasi Algoritma Artificial Neural Network dengan Aktivasi ReLU: Klasifikasi Tiroid,” JURNAL SWABUMI, vol. 12, no. 1, p. 2024, 2024.
[19] A. F. Agarap, “Deep Learning using Rectified Linear Units (ReLU),” Mar. 2018, [Online]. Available: http://arxiv.org/abs/1803.08375
[20] S. R. Dubey, S. K. Singh, and B. B. Chaudhuri, “Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark,” Sep. 2021, [Online]. Available: http://arxiv.org/abs/2109.14545
[21] J. Terven, D. M. Cordova-Esparza, A. Ramirez-Pedraza, and E. A. Chavez-Urbiola, “Loss Functions and Metrics in Deep Learning,” Jul. 2023, [Online]. Available: http://arxiv.org/abs/2307.02694
[22] R. R. Akbar, B. Rahmat, and A. Junaidi, “Implementasi Algoritma Convolutional Neural Network Pada Transliterasi Aksara Jawa Ke Aksara Latin Dengan Penerapan Fungsi Hinge Loss,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3S1, Oct. 2024, doi: 10.23960/jitet.v12i3S1.5350.
[23] L. Zhao, “A Robust Loss Function for Multiclass Classification,” Int J Mach Learn Comput, vol. 3, no. 6, pp. 462–467, Dec. 2013, doi: 10.7763/ijmlc.2013.v3.361.
[24] E. Setia Budi, A. Nofriyaldi Chan, P. Priscillia Alda, and M. Arif Fauzi Idris, “RESOLUSI: Rekayasa Teknik Informatika dan Informasi Optimasi Model Machine Learning untuk Klasifikasi dan Prediksi Citra Menggunakan Algoritma Convolutional Neural Network,” Media Online, vol. 4, no. 5, pp.509, 2024, [Online]. Available: https://djournals.com/resolusi
[25] M. Irfan Yusuf, A. Wahyu Widodo, V. Wardhani, and B. University, “COVID-19 Detection on Chest X-Ray Images Using Modified VGG-19,” 2023. [Online]. Available: www.jitecs.ub.ac.id
[26] A. M. Carrington et al., “Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation,” Mar. 2021, doi: 10.1109/TPAMI.2022.3145392.
[27] B. G. Gaji´c, A. Amato, R. Baldrich, and C. Gatta, “Area Under the ROC Curve Maximization for Metric Learning.”
[28] S. Single, S. Iranmanesh, and R. Raad, “RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning,” Information (Switzerland), vol. 14, no. 12, Dec. 2023, doi: 10.3390/info14120633.
[29] Khadijah, S. N. Endah, R. Kusumaningrum, Rismiyati, P. S. Sasongko, and I. Z. Nisa, “Solid waste classification using pyramid scene parsing network segmentation and combined features,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 19, no. 6, pp. 1902–1912, 2021, doi: 10.12928/TELKOMNIKA.v19i6.18402.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Apriandy Angdresey, Indah Yessi Kairupan, Andre Gabriel Mongkareng
This work is licensed under a Creative Commons Attribution-ShareAlike 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 a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement 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 acknowledgement 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 (See The Effect of Open Access).
