Klasifikasi Sampah di Saluran Air Menggunakan Algortima CNN
Abstract
Tingginya populasi manusia turut menyumbangkan peningkatan jumlah sampah, sehingga dibutuhkan sebuah sistem yang membantu manusia mengklasifikasikan sampah. Perkembangan teknologi yang dirasakan hampir di semua aspek kehidupan termasuk pada pengembangan lingkungan. Dengan teknologi yang ada diharapkan bisa membantu meringankan tugas manusia dan meningkatkan efektifitas penggunaan waktu. Convolutional Neural Network (CNN) merupakan sebuah sistem pengolahan objek dengan pengenalan citra. Dan dengan menggunakan teknik CNN atau Convolutional Neural Network yang banyak digunakan untuk mengenali suatu objek dan diharapkan dapat mempermudah kerja manusia serta menghemat waktu yang digunakan.
Downloads
References
C. Xianbao, Q. Guihua, J. Yu, and Z. Zhaomin, “An improved small object detection method based on Yolo V3,” Pattern Anal. Appl., vol. 24, no. 3, pp. 1347–1355, 2021, doi: 10.1007/s10044-021-00989-7.
M. B. Bejiga, A. Zeggada, A. Nouffidj, and F. Melgani, “A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery,” Remote Sens., vol. 9, no. 2, 2017, doi: 10.3390/rs9020100.
Y. Liu, P. Sun, N. M. Wergeles, and Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,” Expert Syst. Appl., 2021, doi: 10.1016/j.eswa.2021.114602.
Y. Liu, P. Sun, N. Wergeles, and Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,” Expert Syst. Appl., vol. 172, no. April 2020, p. 114602, 2021, doi: 10.1016/j.eswa.2021.114602.
K. Židek, A. Hosovsky, J. Piteľ, and S. Bednár, “Recognition of Assembly Parts by Convolutional Neural Networks,” Lect. Notes Mech. Eng., pp. 281–289, 2019, doi: 10.1007/978-3-319-99353-9_30.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.
R. Girshick, “Fast R-CNN,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2015 Inter, pp. 1440–1448, 2015, doi: 10.1109/ICCV.2015.169.
C. Cao et al., “An Improved Faster R-CNN for Small Object Detection,” IEEE Access, 2019, doi: 10.1109/access.2019.2932731.
J. Noh, W. Bae, W. Lee, J. Seo, and G. Kim, “Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2019-Octob, pp. 9724–9733, 2019, doi: 10.1109/ICCV.2019.00982.
S. M. A. Bashir and Y. Wang, “Small object detection in remote sensing images with residual feature aggregation-based super-resolution and object detector network,” Remote Sens., vol. 13, no. 9, 2021, doi: 10.3390/rs13091854.
Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu, “Traffic-Sign Detection and Classification in the Wild,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 2110–2118, 2016, doi: 10.1109/CVPR.2016.232.
Y. Liu et al., “Detecting Cancer Metastases on Gigapixel Pathology Images,” pp. 1–13, 2017, [Online]. Available: http://arxiv.org/abs/1703.02442.
Y. Liu, F. Yang, and P. Hu, “Small-Object Detection in UAV-Captured Images via Multi-Branch Parallel Feature Pyramid Networks,” IEEE Access, 2020, doi: 10.1109/access.2020.3014910.
O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015, doi: 10.1007/s11263-015-0816-y.
H. Azis, F. Tangguh Admojo, and E. Susanti, “Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah,” Techno.Com, vol. 19, no. 3, pp. 286–294, 2020.
Copyright (c) 2022 Indonesian Journal of Data and Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- The work is not under consideration for publication elsewhere.
- The work has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Indonesian Journal of Data and Science agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. (CC BY-NC 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.