Implementation of YOLO V8 Algorithm in Organic and Anorganic Waste Detection Application for Waste to Energy Management
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Jun 30, 2025
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Abstract
Waste management in Indonesia is a major challenge, especially in the development of waste to energy (WTE). Accurate classification of organic and inorganic waste is required to optimise energy conversion. This research develops an automated waste detection system in temporary landfill sites (TPS) using the YOLOv8 algorithm, known for its high speed and accuracy. The research involved data collection, development of a YOLOv8-based computational model, and system construction and testing according to field requirements. The results show that YOLOv8 has high performance in detecting organic and inorganic waste, with 99.35% accuracy, 98.6% precision, 98.6% recall and 98.5% F1 score. This system can speed up the waste sorting process and has the potential to be used in domestic and public environments for the automatic detection of waste categories.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Arvio, Y., Kusuma, D. T., & Sangadji, I. B. (2025). Implementation of YOLO V8 Algorithm in Organic and Anorganic Waste Detection Application for Waste to Energy Management. Jurnal E-Komtek, 9(1), 169-181. https://doi.org/10.37339/e-komtek.v9i1.2310
References
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[12] J. H. Kim, N. Kim, and C. S. Won, “High-Speed Drone Detection Based On Yolo-V8,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, pp. 1–2, 2023, doi: 10.1109/ICASSP49357.2023.10095516.
[13] A. Naureen, K. C. Krishna, N. S. Teja, L. Mahesh, and K. Vamshi, “College Bus Number Plate Registration Detection Using YOLO-V8,” 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT), pp. 1–7, 2024, doi: 10.1109/easct59475.2023.10392472.
[14] F. M. Talaat and H. ZainEldin, “An improved fire detection approach based on YOLO-v8 for smart cities,” Neural Comput Appl, vol. 35, no. 28, pp. 20939–20954, Oct. 2023, doi: 10.1007/s00521-023-08809-1.
[15] M. G. Ragab et al., “A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023),” IEEE Access, vol. 12, no. April, pp. 57815–57836, 2024, doi: 10.1109/ACCESS.2024.3386826.
[16] P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,” Procedia Comput Sci, vol. 199, pp. 1066–1073, 2021, doi: 10.1016/j.procs.2022.01.135.
[2] H. M. Ahmad and A. Rahimi, “Deep learning methods for object detection in smart manufacturing: A survey,” J Manuf Syst, vol. 64, pp. 181–196, 2022, doi: https://doi.org/10.1016/j.jmsy.2022.06.011.
[3] D. H C, “An Overview of You Only Look Once: Unified, Real-Time Object Detection,” Int J Res Appl Sci Eng Technol, vol. 8, no. 6, pp. 607–609, Jun. 2020, doi: 10.22214/ijraset.2020.6098.
[4] Y. Arvio, D. T. Kusuma, I. Sangadji, and E. K. Dewantara, “Penerapan Metode Convolution Neural Network ( CNN ) Dalam Proses Pengolahan Citra Untuk Mendeteksi Cacat Produksi Pada Produk Masker,” vol. 16, no. 4, pp. 340–350, 2023, doi: 10.30998/faktorexacta.v16i4.20073.
[5] Y. Arvio, D. Tiara Kusuma, and I. B. Sangadji, “PENDEKATAN ALGORITMA YOLO V5 UNTUK MENDETEKSI CACAT PRODUK MASKER YOLO V5 ALGORITHM APPROCH FOR THE FACE MASK DEFECT DETECTION,” 2023. [Online]. Available: http://jurnal.dinarek.unsoed.ac.id
[6] O. Soerya, N. Utomo, F. Utaminingrum, and E. R. Widasari, “Implementasi YOLO versi 3 untuk Mengidentifikasi dan Mengklasifikasi Sampah Kantor berbasis NVIDIA Jetson Nano,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 6, pp. 2829–2834, 2022, [Online]. Available: http://j-ptiik.ub.ac.id
[7] T. J. Ichsan, M. Kom. , Tedi Gunawan, S.T., and MT. , Rini Handayani, S.ST., “Prototipe Pemilah Sampah Organik Dan Nonorganik,” e-Proceeding of Applied Science, vol. 5, No.3, no. Desember 2019, p. 2426, 2019, [Online]. Available: https://core.ac.uk/download/pdf/299935198.pdf
[8] J. R. Terven and D. M. Cordova-Esparza, “A COMPREHENSIVE REVIEW OF YOLO ARCHITECTURES IN COMPUTER VISION: FROM YOLOV1 TO YOLOV8 AND YOLO-NAS PUBLISHED AS A JOURNAL PAPER AT MACHINE LEARNING AND KNOWLEDGE EXTRACTION,” 2023.
[9] M. Hussain, “YOLOv1 to v8: Unveiling Each Variant-A Comprehensive Review of YOLO,” IEEE Access, vol. 12, pp. 42816–42833, 2024, doi: 10.1109/ACCESS.2024.3378568.
[10] M. H. Wang et al., “Optimizing Real-Time Trichiasis Object Detection: A Comparative Analysis of YOLOv5 and YOLOv8 Performance Metrics,” in 2023 9th International Conference on Systems and Informatics (ICSAI), 2023, pp. 1–5. doi: 10.1109/ICSAI61474.2023.10423285.
[11] A. A. Rasjid, B. Rahmat, and A. N. Sihananto, “Implementasi YOLOv8 Pada Robot Deteksi Objek,” Journal of Technology and System Information, vol. 1, no. 3, p. 9, Jul. 2024, doi: 10.47134/jtsi.v1i3.2969.
[12] J. H. Kim, N. Kim, and C. S. Won, “High-Speed Drone Detection Based On Yolo-V8,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, pp. 1–2, 2023, doi: 10.1109/ICASSP49357.2023.10095516.
[13] A. Naureen, K. C. Krishna, N. S. Teja, L. Mahesh, and K. Vamshi, “College Bus Number Plate Registration Detection Using YOLO-V8,” 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT), pp. 1–7, 2024, doi: 10.1109/easct59475.2023.10392472.
[14] F. M. Talaat and H. ZainEldin, “An improved fire detection approach based on YOLO-v8 for smart cities,” Neural Comput Appl, vol. 35, no. 28, pp. 20939–20954, Oct. 2023, doi: 10.1007/s00521-023-08809-1.
[15] M. G. Ragab et al., “A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023),” IEEE Access, vol. 12, no. April, pp. 57815–57836, 2024, doi: 10.1109/ACCESS.2024.3386826.
[16] P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,” Procedia Comput Sci, vol. 199, pp. 1066–1073, 2021, doi: 10.1016/j.procs.2022.01.135.