Examining Performance of Naïve Bayes and Support Vector Machine for Solid Waste Classification in Automated Sorting Systems
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2025.19.2.5Keywords:
automated sorting system, image classification, Naïve Bayes, pollution, solid waste management, Support Vector MachineAbstract
The growing volume of global waste poses significant challenges to effective waste management, underscoring the need for innovative classification methods to improve recycling efficiency. This study evaluates the performance of two traditional machine learning models, Nae Bayes and Support Vector Machines (SVMs), for classifying solid waste materials in an automated sorting system. A dataset of 284 JPEG images, categorized into five classes (cardboard, glass, metal, paper, and plastic), was utilized. Preprocessing involved resizing images to 512x384 pixels, normalizing pixel values, and extracting features using Histograms of Oriented Gradients (HOG) and Color Histograms. Nae Bayes demonstrated computational efficiency with 98.90% accuracy and an F1-score of 0.908, but struggled with overlapping features, leading to misclassifications, particularly between glass and metal. In contrast, SVM outperformed Nae Bayes, achieving 99.80% accuracy and an F1-score of 0.965 by effectively handling complex, overlapping features via optimal decision boundaries. The findings highlight SVM?s superior performance for complex datasets, while Nae Bayes remains a viable option for simpler tasks. This study underscores the potential of traditional machine learning in waste classification. However, it suggests that integrating deep learning models could improve accuracy, scalability, and adaptability in real-world waste-sorting systems.
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Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F., What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. World Bank Publications, 2018. DOI: 10.1596/978-1-4648-1329-0
Didier, S., & Kamuhanda, J., The Impacts of Waste Management Practices on Environmental and Social Well-Being in Secondary Schools of Musanze District, Rwanda. East African Journal of Environment and Natural Resources, 8(2), pp. 167-177, 2025. DOI: 10.37284/eajenr.8.2.3171
Park, S., Choi, S., & Lah, T.J., Assessing the Determinants of Perceived Recycling Inconvenience in South Korea. International Journal of Environment and Sustainable Development, 23(1), pp. 57-73, 2024. DOI: 10.1504/IJESD.2024.135640
Coracero, E.E., Gallego, R.J., Frago, K.J.M. & Gonzales, R.J.R., A Long-Standing Problem: A Review of Solid Waste Management in the Philippines. Indonesian Journal of Social and Environmental Issues, 2(3), pp. 213-220, 2021. DOI: 10.47540/ijsei.v2i3.144
Lakhouit, A., Revolutionizing Urban Solid Waste Management with AI and Iot: A Review of Smart Solutions for Waste Collection, Sorting, and Recycling. Results in Engineering, 25, 104018, 2025. DOI: 10.1016/j.rineng.2025.104018
Li, C.H., Lee, T.T. & Lau, S.S.Y., Enhancement of Municipal Solid Waste Management in Hong Kong through Innovative Solutions: A Review, Sustainability, 15(4), 3310, 2023. DOI: 10.3390/su15043310
Li, M., Ma, Z., Yan, R. & Yin, J., The Impact of Physiological and Psychological Fatigue on Work Efficiency: A Case Study of Parcel Sorting Work. Sensors, 24(18), 5989, 2024. DOI: 10.3390/s24185989
Krystosik, A., Njoroge, G., Odhiambo, L., Forsyth, J.E., Mutuku, F. & LaBeaud, A.D., Solid Wastes Provide Breeding Sites, Burrows, and Food for Biological Disease Vectors and Urban Zoonotic Reservoirs: A Call to Action for Solutions-Based Research, Frontiers in Public Health, 7, 405, 2020. DOI: 10.3389/fpubh.2019.00405
Srivastava, R.R., Rajak, D.K., Ilyas, S., Kim, H. & Pathak, P., Challenges, Regulations, and Case Studies on Sustainable Management of Industrial Waste, Minerals, 13(1), 51, 2022. DOI: 10.3390/min13010051
Palagan, C.A., Joe, S.S.A., Mary, S.J. & Jijo, E.E., Predictive Analysis-Based Sustainable Waste Management in Smart Cities Using IoT Edge Computing and Blockchain Technology, Computers in Industry, 166, 104234, 2025. DOI:.1016/j.compind.2024.104234
Li, N., & Chen, Y., Municipal Solid Waste Classification and Real-Time Detection Using Deep Learning Methods, Urban Climate, 49, 101462, 2023. DOI: 10.1016/j.uclim.2023.101462
Yulita, I.N., Ardiansyah, F., Prabuwono, A.S., Ramdhani, M.R., Wicaksono, M.A., Trisanto, A. & Sholahuddin, A., Recyclable waste classification using SqueezeNet and XGBoost, International Journal of Advanced Computer Science and Applications, 14(10), pp. 345-352, 2023. DOI: 10.14569/IJACSA.2023.0141037
Hanbal, I.F., Ingosan, J.S., Oyam, N.A.A. & Hu, Y., Classifying Wastes Using Random Forests, Gaussian Nae Bayes, Support Vector Machine, and Multilayer Perceptron. IOP Conference Series: Materials Science and Engineering, 803(1), 012017, 2020. DOI: 10.1088/1757-899X/803/1/012017
Chowdhury, A.T., Chowdhury, M.E., Islam, M.S.B., Newaz, M., Salam, A., Sayem, F. & Ashraf, A. , Chapter 18 - Intelligent Waste Management: A Comprehensive Review of Machine Learning and Deep Learning Applications in Advanced Recycling, in Sadasivuni, K.K., Bacanin, N., Kim, J. & Vashisht, N.B., Harnessing Automation and Machine Learning for Resource Recovery and Value Creation, pp. 427-460, 2025. DOI: 10.1016/B978-0-443-27374-2.00018-2
Poudel, S. & Poudyal, P., Classification of Waste Materials Using CNN Based on Transfer Learning, in Proceedings of the Forum for Information Retrieval Evaluation (FIRE ?22), pp. 1?5, 2022. ACM. DOI: 10.1145/3574318.3574345
Abood, I.N. & Al-Talib, G.A.A., Waste Classification Using Artificial Intelligence Techniques: A Literature Review. Technium, 5, pp. 49-59, 2023. DOI: 10.47577/technium.v5i.8345
Namoun, A., Tufail, A., Khan, M.Y., Alrehaili, A., Syed, T.A. & BenRhouma, O., Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges. Sustainability, 14(20), 13578, 2022. DOI: 10.3390/su142013578
Islam, M.M., Hasan, S.M., Hossain, M.R., Uddin, M.P. & Mamun, M.A., Towards Sustainable Solutions: Effective Waste Classification Framework via Enhanced Deep Convolutional Neural Networks, Plos One, 20(6), e0324294, 2025. DOI: 10.1371/journal.pone.0324294
Azizah, W., Feature Extraction Using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection, Jurnal SISFOKOM (Sistem Informasi dan Komputer), 14(1), pp. 8-14, 2025.
Wang, P., Fan, E. & Wang, P., Comparative Analysis of Image Classification Algorithms Based on Traditional Machine Learning and Deep Learning. Pattern Recognition Letters, 141, pp. 61-67, 2021. DOI: 10.1016/j.patrec.2020.07.042
Palanimeera, J. & Ponmozhi, K., Classification of Yoga Pose Using Machine Learning Techniques, Materials Today: Proceedings, 37, pp. 2930-2933, 2021. DOI: 10.1016/j.matpr.2020.08.700
Permana, A.A. & Arsanah, A., Enhancing Early Diagnosis of Heart Disease: A Comparative Study of K-NN and Nae Bayes Classifiers Using the UCI Heart Disease Dataset, Journal of Intelligent Computing & Health Informatics, 5(1), pp. 35-42, 2024. DOI: 10.26714/jichi.v5i1.11251
Likmi, S., Comparative Analysis of Nae Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) Algorithms for Classification of Heart Disease Patients, JOIN (Jurnal Online Informatika), 7(2), pp. 219-225, 2022. DOI: 10.15575/join.v7i2.919
Ahsan, M.M., Luna, S.A. & Siddique, Z., Machine-Learning-Based Disease Diagnosis: A Comprehensive Review. Healthcare, 10(3), 541, 2022. DOI: 10.3390/healthcare10030541
Trigka, M. & Dritsas, E., A Comprehensive Survey of Deep Learning Approaches in Image Processing. Sensors, 25(2), 531, 2025. DOI: 10.3390/s25020531
Kumar, P. R. & Vijaya, A., Nae Bayes Machine Learning Model for Image Classification to Assess the Level of Deformation of Thin Components, Materials Today: Proceedings, 68, 2265?2274, 2022. DOI: 10.1016/j.matpr.2022.08.489
Ronald, M., & Evans, M., Classification of Selected Apple Fruit Varieties Using Nae Bayes, Indian Journal of Computer Science and Engineering, 7(1), 13?19, 2016.
Priyanthy, S. & Naleer, H.M.M., Handwritten Character Recognition Using Nae Bayes Classifier Method for Desktop and Mobile Applications, in Proceedings of the Fifth Annual Science Research Sessions 2016: Enriching the Novel Scientific Research for The Development of the Nation, pp. 104?113, 2016. Faculty of Applied Sciences, South Eastern University of Sri Lanka.
Santhuja, P. & Suresh, A., Identifying Toxic Components in E-Waste Using IoT and Nae Bayes Classification, in Proceedings of the 5th International Conference on Soft Computing for Security Applications (ICSCSA 2025) pp. 961-966, 2025 https://doi.org/10.1109/ICSCSA66339.2025.11170870
Zhou, Z.-H. & Feng, J., Deep Forest: Towards an Alternative to Deep Neural Networks, in Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 3553?3559, 2017. DOI: 10.24963/ijcai.2017/497
Abdullah, D.M. & Abdulazeez, A.M. Machine Learning Applications Based on SVM Classification: A Review, Qubahan Academic Journal, 1(2), pp. 81-90, 2021. DOI: 10.48161/qaj.v1n2a50
Chandra, M.A. & Bedi, S.S., Survey on SVM and Their Application in Image Classification, International Journal of Information Technology, 13(5), pp. 1-11, 2021. DOI: 10.1007/s41870-017-0080-1
Thilagavathi, K., Kavitha, K., Praba, R.D. Arina, S.V. & Sahana, R.C., Detection of Diseases in Sugarcane Using Image Processing Techniques, Bioscience Biotechnology Research Communications, 13(11), pp. 109-115, 2020. DOI: 10.21786/bbrc/13.11/24
Rer, C., Bling, K., Hunsche, M., Rumpf, T., Noga, G. & Pler, L., Robust Fitting of Fluorescence Spectra for Pre-Symptomatic Wheat Leaf Rust Detection with Support Vector Machines, Computers and Electronics in Agriculture, 79(2), pp. 180-188, 2011. DOI: 10.1016/j.compag.2011.09.011
Vabalas, A., Gowen, E., Poliakoff, E. & Casson, A.J., Machine Learning Algorithm Validation with a Limited Sample Size. Plos One, 14(11), e0224365, 2019. DOI: 10.1371/journal.pone.0224365
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., & Fei-Fei, L., Imagenet Large-Scale Visual Recognition Challenge, International Journal of Computer Vision, 115(3), pp. 211-252, 2015.
Powers, D.M.W., Evaluation: From Precision, Recall, and F-Measure to ROC, Informedness, Markedness, and Correlation, 2020. DOI: 10.48550/arXiv.2010.16061
Dalal, N. & Triggs, B., Histograms of Oriented Gradients for Human Detection, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
Schkopf, B. & Smola, A.J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, 2002.
Purnomo, A., Barata, M.A., Soeleman, M.A. & Alzami, F., Adding Feature Selection on Nae Bayes to Increase Accuracy in Classifying Heart Attack Disease, Journal of Physics: Conference Series, 1511(1), 012001, 2020. DOI: 10.1088/1742-6596/1511/1/012001
Kesumawati, A. & Utari, D.T., Predicting patterns of student graduation rates using nae Bayes classifier and support vector machine. AIP Conference Proceedings, 2021(1), 060005, 2018. DOI: 10.1063/1.5062769
