Klasifikasi Benda Organik dan Anorganik Dengan Metode YOLOv3 dan ResNet50

Kevin Reynaldi Tanjung, Liliana Liliana, Hans Juwiantho


There are still many Indonesian people throw waste in the wrong place. One of the reasons is that there are still many Indonesian people who still find it difficult to sort organic and inorganic objects. Therefore, the introduction of organic and inorganic objects is very important and we need something that can help in sorting organic and inorganic objects. By knowing the difference between organic and inorganic objects, people can sort out organic and inorganic waste. The methods used are You Only Look Once to get waste objects from an images or videos. The detected object will be cut and the results will be processed by the Convolutional Neural Network with the ResNet50 architectural model for classification. In the YOLOv3 and ResNet50 training process, adjustments are made to find parameters to get best accuracy This research will classify objects on waste objects in images or videos. The Mean Average Precision obtained by YOLOv3 is 45% and the average loss is 91%. For ResNet50 there is rule of thumb where when using input size 416x416 and the lower the number of learning rates can increase accuracy. When combined, ResNet50 is able to increase the accuracy of the detected object types by YOLOv3.


Organic; Inorganic; YOLOv3; Convolutional Neural Network; ResNet50; Classify Object.

Full Text:



Divvala, S., Redmon, J., Girshick, R., & Farhadi, A. (2016).

You only look once: Unified, real-time object detection.

In Proceedings of the IEEE conference on computer vision

and pattern recognition (pp. 779-788). DOI:


Farhadi, A. & Redmon, J. (2018). Yolov3: An incremental

improvement. arXiv preprint arXiv:1804.02767.

Haque, M. F., Lim, H. Y., & Kang, D. S. (2019, January).

Object detection based on VGG with ResNet network.

In 2019 International Conference on Electronics,

Information, and Communication (ICEIC) (pp.1-3). IEEE.

DOI: 10.23919/ELINFOCOM.2019.8706476.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual

learning for image recognition. In Proceedings of the IEEE

conference on computer vision and pattern recognition (pp.

-778). DOI: 10.1109/CVPR.2016.90.

Kulkarni, H. N., & Raman, N. K. S. (2019). Waste object

detection and classification. In CS230 Stanford.

Liu, Xinchao & Gan, Haiyun & Yan, Ying. (2021). Study

on Improvement of YOLOv3 Algorithm. Journal of Physics:

Conference Series. 1884. 012031. 10.1088/1742-

/1884/1/012031. DOI : 1088/1742-


Mahmud, K. H., Adiwijaya, A., & Al Faraby, S. (2019).

Klasifikasi Citra Multi-Kelas Menggunakan Convolutional

Neural Network. eProceedings of Engineering, 6(1). ISSN :


Mulyadi, S., & Budiraharjo, R. (2019, July 24).



Proceedings of Conference on Information Technology and

Electrical Engineering. ISSN : 2085-6350.

Nwankpa, C., Ijomah, W., Gachagan, A., & Marshall, S.

(2018). Activation functions: Comparison of trends in

practice and research for deep learning. arXiv preprint


Van Etten, A. (2019, January). Satellite imagery multiscale

rapid detection with windowed networks. In 2019 IEEE

winter conference on applications of computer vision

(WACV) (pp. 735-743). IEEE. arXiv preprint



  • There are currently no refbacks.

Jurnal telah terindeks oleh :