Article Sidebar

Published
March 30, 2026
Download
pdf
Statistic
Read Counter : 36 Download : 62

Main Article Content

Abstract

The problem of waste management continues to increase along with population growth and lifestyle changes, highlighting the need for a fast and accurate waste classification system to support recycling processes. This study implements a transfer learning approach using seven Convolutional Neural Network (CNN) architectures: MobileNet, MobileNetV2, Xception, EfficientNetB0, VGG16, VGG19, and ResNet50 to classify waste into two categories: organic and recyclable. Each model is modified by adding a Global Average Pooling layer followed by a fully connected layer with 256 neurons before the output layer. The models are trained twice using 30 epochs, a batch size of 2, the Adam optimizer, and a learning rate of 0.0001. Experimental results show that ResNet50 achieves the best performance, with an accuracy of 89.84%, precision of 96.34%, recall of 82.82%, and an F1-score of 89.07%, followed by MobileNet with an accuracy of 89.25%. In contrast, Xception demonstrates the lowest performance, with an accuracy of 83.81%. Analysis of training and validation curves indicates that ResNet50 and MobileNet exhibit better stability and lower overfitting tendencies compared to other models.

Keywords

CNN global average pooling organic recyclable transfer learning

Article Details

Copyright (c) 2026 Nur Nafiiyah, M. Ari Zulkarnaen, Agus Harjoko, Achmad Nizar Hidayanto

Creative Commons License

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

References

  1. Agustiani, S., Junaidi, A., Aryanti, R., Abdul, A., & Kamil, B. (2025). Waste Classification using EfficientNetB3-Based Deep Learning for Supporting Sustainable Waste Management. Bulletin of Informatics and Data Science, 4(1), 62–70. https://doi.org/10.61944/bids.v4i1.108
  2. Alamsyah, R., Tarigan, I. J., & Yap, R. (2023). Klasifikasi Jenis Sampah dengan Metode Gray Level Co-Occurence Matrix (GLCM) dan Support Vector Machine (SVM). Jurnal Armada Informatika, 7(2), 342–352. http://jurnal.stmikmethodistbinjai.ac.id/jai/article/view/85
  3. Altikat, A., Gulbe, A., & Altikat, S. (2022). Intelligent solid waste classification using deep convolutional neural networks. International Journal of Environmental Science and Technology, 19(3). https://doi.org/10.1007/s13762-021-03179-4
  4. Ananda, G. F., & Setyawan, H. (2026). Deep Learning-Based Waste Classification with Transfer Learning Using. JURNAL RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(4), 535–541.
  5. Arifin, F., Habiburrahman, M., & Gusti, W. R. (2023). Classification of Organic and Inorganic Waste Types Based on Neural Networks. Elinvo (Electronics, Informatics, and Vocational Education), 8(1). https://doi.org/10.21831/elinvo.v8i1.53284
  6. Chhabra, M., Sharan, B., Elbarachi, M., & Kumar, M. (2024). Intelligent waste classification approach based on improved multi-layered convolutional neural network. Multimedia Tools and Applications, 83(36), 84095–84120. https://doi.org/10.1007/s11042-024-18939-w
  7. Dahyoung Yenuargo, Muhamad Fatchan, & Wahyu Hadikristanto. (2024). Valuation of Svm Kernel Performance in Organic and Non-Organic Waste Classification. International Journal of Integrated Science and Technology, 2(5), 494–505. https://doi.org/10.59890/ijist.v2i5.1873
  8. Ety Sutanty, Maukar, Dina Kusuma Astuti, & Handayani. (2023). Penerapan Model Arsitektur VGG16 Untuk Klasifikasi Jenis Sampah. Decode: Jurnal Pendidikan Teknologi Informasi, 3(2). https://doi.org/10.51454/decode.v3i2.331
  9. Ibnul Rasidi, A., Pasaribu, Y. A. H., Ziqri, A., & Adhinata, F. D. (2022). Klasifikasi Sampah Organik dan Non-Organik Menggunakan Convolutional Neural Network. Jurnal Teknik Informatika Dan Sistem Informasi, 8(1). https://doi.org/10.28932/jutisi.v8i1.4314
  10. Islam, M. S. Bin, Sumon, M. S. I., Majid, M. E., Abul Kashem, S. Bin, Nashbat, M., Ashraf, A., Khandakar, A., Kunju, A. K. A., Hasan-Zia, M., & Chowdhury, M. E. H. (2025). ECCDN-Net: A deep learning-based technique for efficient organic and recyclable waste classification. Waste Management, 193(March 2024), 363–375. https://doi.org/10.1016/j.wasman.2024.12.023
  11. Kurniawan, R., Wintoro, P. B., Mulyani, Y., & Komarudin, M. (2023). Implementasi Arsitektur Xception Pada Model Machine Learning Klasifikasi Sampah Anorganik. In Jurnal Informatika dan Teknik Elektro Terapan (Vol. 11, Issue 2). https://doi.org/10.23960/jitet.v11i2.3034
  12. Li, N., & Chen, Y. (2023). Municipal solid waste classification and real-time detection using deep learning methods. Urban Climate, 49. https://doi.org/10.1016/j.uclim.2023.101462
  13. Malik, M., Sharma, S., Uddin, M., Chen, C. L., Wu, C. M., Soni, P., & Chaudhary, S. (2022). Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models. Sustainability (Switzerland), 14(12). https://doi.org/10.3390/su14127222
  14. Muslihati, Sahibu, S., & Taufik, I. (2024). Implementation of the Convolutional Neural Network Algorithm for Classifying Types of Organic and Non-Organic Waste. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(3), 840–852.
  15. Nisa, I. Z., Endah, S. N., Sasongko, P. S., Kusumaningrum, R., Khadijah, K., & Rismiyati, R. (2022). Klasifikasi Citra Sampah Menggunakan Support Vector Machine dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix dan Color Moments. Jurnal Teknologi Informasi Dan Ilmu Komputer, 9(5). https://doi.org/10.25126/jtiik.2022954868
  16. Sadida Aulia, D., Arwoko, H., & Asmawati, E. (2024). Klasifikasi Sampah Rumah Tangga Menggunakan Metode Convolutional Neural Network. Metik Jurnal, 8(2), 114–120. https://doi.org/10.47002/metik.v8i2.956
  17. Santoso, B. D., & Nafi’iyah, N. (2024). Garbage Image Classifier using Modified ResNet-50. Telematika, 17(2), 84–94. https://doi.org/10.35671/telematika.v17i2.2873
  18. Santoso, H., Hanif, I., Magdalena, H., & Afiyati. (2024). A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction. International Journal on Informatics Visualization, 8(2), 623–634. https://doi.org/10.62527/joiv.8.2.1943
  19. Toğaçar, M., Ergen, B., & Cömert, Z. (2020). Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models. Measurement: Journal of the International Measurement Confederation, 153. https://doi.org/10.1016/j.measurement.2019.107459
  20. Wedha, B. Y., Sholihati, I. D., & Ningsih, S. (2024). Implementation Convolutional Neural Network for Visually Based Detection of Waste Types. Journal of Computer Networks, Architecture and High Performance Computing, 6(1). https://doi.org/10.47709/cnahpc.v6i1.3427
  21. Zhang, Q., Yang, Q., Zhang, X., Bao, Q., Su, J., & Liu, X. (2021). Waste image classification based on transfer learning and convolutional neural network. Waste Management, 135. https://doi.org/10.1016/j.wasman.2021.08.038