Optimasi Algoritma Convolutional Neural Network dengan Arsitektur Efficientnet-B0 dan Resnet-50 untuk Klasifikasi Jenis Sampah: Optimization of Convolutional Neural Network Algorithm with Efficientnet-B0 and Resnet-50 Architecture for Waste Type Classification

Wildan Muhammad Ardana; Kusrini Kusrini

doi:10.57152/malcom.v5i4.2030

Authors

Wildan Muhammad Ardana Universitas Amikom Yogyakarta
Kusrini Kusrini Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.57152/malcom.v5i4.2030

Keywords:

Augmentasi Data, Convolutional Neural Network, EfficientNet-B0, Klasifikasi Sampah

Abstract

Penelitian ini mengembangkan sistem klasifikasi sampah otomatis menggunakan deep learning untuk membedakan sampah organik dan dapat didaur ulang. Penelitian dilakukan menggunakan dataset dari Kaggle yang terdiri dari 25.077 gambar sampah dengan dua kategori utama: organik (O) dan dapat didaur ulang (R). Metodologi penelitian berfokus pada perbandingan transfer learning menggunakan arsitektur EfficientNet-B0 dan ResNet-50. Teknik data augmentation modern (rotasi, zoom, flip) diterapkan untuk meningkatkan generalisasi model, dan Keras Tuner digunakan untuk optimasi hyperparameter secara sistematis. Hasil penelitian menunjukkan bahwa model EfficientNet-B0, setelah optimasi hyperparameter, mencapai performa terbaik dengan akurasi pengujian 97.25%. Arsitektur ini secara signifikan mengungguli ResNet-50 (akurasi 93.39%) dalam skenario perbandingan. Laporan klasifikasi detail untuk model terbaik menunjukkan kinerja yang sangat baik dan seimbang dalam mengklasifikasi sampah organik (presisi: 0.93, recall: 0.98) dan sampah dapat didaur ulang (presisi: 0.97, recall: 0.91). Waktu evaluasi yang cepat mengindikasikan potensi implementasi sistem secara real-time. Penelitian ini membuktikan efektivitas transfer learning dengan arsitektur modern yang dikombinasikan dengan optimasi hyperparameter untuk menciptakan solusi klasifikasi sampah otomatis yang sangat akurat dan efisien.

Downloads

Download data is not yet available.

References

Y. Wang et al., “Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians,” Alzheimers Res Ther, vol. 16, no. 1, p. 175, Aug. 2024, doi: 10.1186/s13195-024-01540-6.

Y. Ardian Pradana, I. Cholissodin, and D. Kurnianingtyas, “Analisis Sentimen Pemindahan Ibu Kota Indonesia pada Media Sosial Twitter menggunakan Metode LSTM dan Word2Vec,” 2023. [Online]. Available: http://j-ptiik.ub.ac.id

Yuli Nurhanisah, “Indonesia Darurat Sampah Plastik di Laut,” Indonesia Baik. Accessed: Oct. 18, 2024. [Online]. Available: https://indonesiabaik.id/infografis/indonesia-darurat-sampah-plastik-laut

Y. Zhou et al., “Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development,” Alexandria Engineering Journal, vol. 108, pp. 415–427, Dec. 2024, doi: 10.1016/j.aej.2024.07.066.

W.-L. Mao, W.-C. Chen, C.-T. Wang, and Y.-H. Lin, “Recycling waste classification using optimized convolutional neural network,” Resour Conserv Recycl, vol. 164, p. 105132, Jan. 2021, doi: 10.1016/j.resconrec.2020.105132.

U. K. Lilhore, S. Simaiya, S. Dalal, and R. Damaševi?ius, “A smart waste classification model using hybrid CNN-LSTM with transfer learning for sustainable environment,” Multimed Tools Appl, vol. 83, no. 10, pp. 29505–29529, Sep. 2023, doi: 10.1007/s11042-023-16677-z.

H. I. Sutomo, “Identification of Organic and Non-Organic Waste with Computer Image Recognition using Convolutionalneural Network with Efficient-Net-B0 Architecture,” Journal of Applied Intelligent System, vol. 8, no. 3, pp. 320–330, Nov. 2023, doi: 10.33633/jais.v8i3.9064.

M. I. B. Ahmed et al., “Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management,” Sustainability, vol. 15, no. 14, p. 11138, Jul. 2023, doi: 10.3390/su151411138.

C. Che, H. Zheng, Z. Huang, W. Jiang, and B. Liu, “Intelligent Robotic Control System Based on Computer Vision Technology,” 2024.

S. Kom. , M. Sc. Dr. Eng. Novanto Yudistira, Deep Learning: Teori, Contoh Perhitungan, dan Implementasi. Deepublish, 2024.

A. W. Salehi et al., “A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope,” Sustainability, vol. 15, no. 7, p. 5930, Mar. 2023, doi: 10.3390/su15075930.

J. Li, Z. Yang, and Y. Luo, “Intention Inference for Space Targets Using Deep Convolutional Neural Network,” Advances in Space Research, Oct. 2024, doi: 10.1016/j.asr.2024.10.006.

H. I. Sutomo, “Identification of Organic and Non-Organic Waste with Computer Image Recognition using Convolutionalneural Network with Efficient-Net-B0 Architecture,” 2023. [Online]. Available: https://www.kaggle.com/datasets/techsash/waste-classification-data.

R. C. Poonia and H. A. Al-Alshaikh, “Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0,” Comput Biol Med, vol. 179, p. 108874, Sep. 2024, doi: 10.1016/j.compbiomed.2024.108874.

A. Jahanbakhshi, M. Momeny, M. Mahmoudi, and P. Radeva, “Waste management using an automatic sorting system for carrot fruit based on image processing technique and improved deep neural networks,” Energy Reports, vol. 7, pp. 5248–5256, Nov. 2021, doi: 10.1016/j.egyr.2021.08.028.

M. Ali, F. P. P., and D. Salama Abd Elminaam, “A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem,” Mathematics, vol. 10, no. 15, p. 2675, Jul. 2022, doi: 10.3390/math10152675.

Sunardi, A. Yudhana, and M. Fahmi, “Improving Waste Classification Using Convolutional Neural Networks: An Application of Machine Learning for Effective Environmental Management,” Revue d’Intelligence Artificielle, vol. 37, no. 4, pp. 845–855, Aug. 2023, doi: 10.18280/ria.370404.

N. Nnamoko, J. Barrowclough, and J. Procter, “Solid Waste Image Classification Using Deep Convolutional Neural Network,” Infrastructures (Basel), vol. 7, no. 4, p. 47, Mar. 2022, doi: 10.3390/infrastructures7040047.

C. Shi, C. Tan, T. Wang, and L. Wang, “A Waste Classification Method Based on a Multilayer Hybrid Convolution Neural Network,” Applied Sciences, vol. 11, no. 18, p. 8572, Sep. 2021, doi: 10.3390/app11188572.

D. Ziouzios, N. Baras, V. Balafas, M. Dasygenis, and A. Stimoniaris, “Intelligent and Real-Time Detection and Classification Algorithm for Recycled Materials Using Convolutional Neural Networks,” Recycling, vol. 7, no. 1, p. 9, Feb. 2022, doi: 10.3390/recycling7010009.