Sistem Deteksi Objek Visual Sampah Organik Dan Anorganik Berbasis Algoritma YOL0v9
-
Dedy Adriansyah
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
- Muhammad Pajar Kharisma Putra Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia (*) Corresponding Author
Abstract
Efficiency in waste management is a major challenge in modern cities. With so many people throwing away organic and inorganic waste, a solution is needed so that the waste can be sorted properly. Therefore, in this research, researchers aim to utilize computer vision technology based on the YOLOv9 algorithm to detect and sort organic and inorganic waste. Using a dataset of 6,747 images from the Roboflow platform, this system was trained to recognize various types of waste using the bounding box labeling method. The YOLOv9 algorithm is equipped with Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) features, which provide superior performance in terms of accuracy and speed of system performance. The model training results show that YOLOv9 has a precision value of 0.83%, recall of 0.85%, and mAP of 0.8%, making the model reliable in detecting objects. However, there are several weaknesses, such as decreasing accuracy in blurry images, overlapping objects, and colors that have similar similarities, which can affect detection results by up to 20-30%. Compared to SSD MobileNet v2, YOLOv9 is superior in accuracy, precision and F-1 Score with results in Accuracy values of 58%, Precision 81%, F1-Score 69%. The Intersection over Union (IoU) test results produce excellent accuracy of 0.96%. This research recommends improvements through data augmentation and sensor integration to improve performance in various lighting conditions. This algorithm has great potential to be applied in technology-based waste management, supporting recycling efficiency, reducing human error, and providing a positive impact on the environment globally.
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References
A. Binawan, “Garbage Care as a Way for Eco-Spiritual Care in a Multifaith Society in Indonesia,” Religions, vol. 14, no. 4, 2023, doi: 10.3390/rel14040509.
N. Fauziyah, S. Sukaris, A. R. Rahim, and R. Jumadi, “Peningkatan Kepedulian Masyarakat Terhadap Lingkungan Khususnya Dalam Permasalahan Sampah,” DedikasiMU(Journal Community Serv., vol. 2, no. 4, p. 561, 2020, doi: 10.30587/dedikasimu.v2i4.2053.
L. Julia Lingga, M. Yuana, N. Aulia Sari, H. Nur Syahida, and C. Sitorus, “Sampah di Indonesia: Tantangan dan Solusi Menuju Perubahan Positif,” Innov. J. Soc. Sci. Res., vol. 4, pp. 12235–12247, 2024.
K. Avitadira, N. Indrawati, and K. Kunci, “Upaya Mengatasi Permasalahan Sampah di DKI Jakarta Tahun 2021 : Tinjauan Collaborative Governance,” NeoRespublica J. Ilmu Pemerintah., vol. 5, no. 1, pp. 49–69, 2023, [Online]. Available: http://neorespublica.uho.ac.id/index.php/journal/article/view/147
M. H. Zhou, S. L. Shen, Y. S. Xu, and A. N. Zhou, “New policy and implementation of municipal solid waste classification in Shanghai, China,” Int. J. Environ. Res. Public Health, vol. 16, no. 17, 2019, doi: 10.3390/ijerph16173099.
N. W. Aisha, “Pengaruh Bank Sampah Terhadap Jumlah Sampah Plastik di Indonesia,” J. Altern. - J. Ilmu Hub. Int., vol. 14, no. 1, pp. 68–73, 2023, doi: 10.31479/jualter.v14i1.57.
H. D. Atmanti, “Kajian Pengelolaan Sampah Di Indonesia,” Pembang. Berkelanjutan di Indones. dalam Mewujudkan Tujuan Ekon. Inklusif, vol. 2, pp. 15–27, 2019.
M. F. Rahman and B. Bambang, “Deteksi Sampah pada Real-time Video Menggunakan Metode Faster R-CNN,” Appl. Technol. Comput. Sci. J., vol. 3, no. 2, pp. 117–125, 2021, doi: 10.33086/atcsj.v3i2.1846.
L. Siswati, H. Eterudin, D. Setiawan, A. T. Ratnaningsih, and A. Yandra, “Penyadaran Kepada Ibu Rumah Tangga dalam Pemisahan Sampah Organik dan Anorganik Rumah Tangga di Kecamatan Minas,” Diklat Rev. J. Manaj. Pendidik. dan Pelatih., vol. 6, no. 1, pp. 94–101, 2022, [Online]. Available: https://ejournal.kompetif.com/index.php/diklatreview/article/view/913
M. A. Ramadhan, “Permasalahan Sampah Menjadi Dampak Lingkungan Tidak Sehat,” J. Kegur. dan Ilmu Pendidik., pp. 1–10, 2022.
K. A. Priana and A. A. I. N. E. Karyawati, “Sistem Pendeteksi Sampah Secara Realtime Menggunakan Metode YOLO,” J. Nas. Teknol. Inf., vol. 2, 2023.
F. Marpaung, F. Aulia, and R. C. Nabila, Computer Vision Dan Pengolahan Citra Digital. Surabaya, Jawa Timur: PUSTAKA AKSARA, 2022. [Online]. Available: www.pustakaaksara.co.id
N. J. Hayati, D. Singasatia, and M. R. Muttaqin, “Object Tracking Menggunakan Algoritma You Only Look Once (YOLO)v8 untuk Menghitung Kendaraan,” Komputa J. Ilm. Komput. dan Inform., vol. 12, no. 2, pp. 91–99, 2023, doi: 10.34010/komputa.v12i2.10654.
S. Youwai, A. Chaiyaphat, and P. Chaipetch, “YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism,” J. Real-Time Image Process., vol. 21, no. 5, pp. 1–21, 2024, doi: 10.1007/s11554-024-01545-2.
A. Ramdan et al., “Implementasi Deteksi Objek Real-Time Sebagai Media Edukasi dengan Algoritma YOLOv8 pada Objek Sampah sampah kota , limbah makanan , limbah ( Sistem Informasi Pengelolaan Sampah Dengan memanfaatkan Artificial Intelligence , kita dapat menciptakan menurut B,” SAINTEKOM, vol. 14, no. 2, pp. 142–153, 2024.
A. Wang et al., “YOLOv10: Real-Time End-to-End Object Detection,” Tsinghua Univ., pp. 1–18, 2024, [Online]. Available: http://arxiv.org/abs/2405.14458
F. Yusuf Raspati, K. Meta, and C. Setianingsih, “Deteksi Sampah Plastik Menggunakan Algoritma Yolov5 (You Only Look Once Version 5),” e-Proceeding Eng., vol. 11, no. 3, pp. 1695–1701, 2024.
O. Soerya, N. Utomo, F. Utaminingrum, and E. R. Widasari, “Implementasi YOLO versi 3 untuk Mengidentifikasi dan Mengklasifikasi Sampah Kantor berbasis NVIDIA Jetson Nano,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 6, pp. 2829–2834, 2022, [Online]. Available: http://j-ptiik.ub.ac.id
R. Theofilus, Rizky and Kurniawan, “Deteksi Sampah di Permukaan Sungai menggunakan Convolutional Neural Network dengan Algoritma YOLOv8,” Semin. Nas. Off. Stat., pp. 537–548, 2024, doi: 10.34123/semnasoffstat.v2024i1.2099.
U. Ramadhan, N. Santoso, and F. Gamar, “Deteksi Sampah Botol Plastik di Perairan Menggunakan YOLO v4- Tiny,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 7, no. 1, pp. 91–98, 2025.
F. Ciaglia, F. S. Zuppichini, P. Guerrie, M. McQuade, and J. Solawetz, “Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark,” 2022, [Online]. Available: http://arxiv.org/abs/2211.13523
J. Fan, L. Cui, and S. Fei, “Waste Detection System Based on Data Augmentation and YOLO_EC,” Sensors, vol. 23, no. 7, 2023, doi: 10.3390/s23073646.
N. T. S. Saptadi, A. Suyuti, A. A. Ilham, and I. Nurtanio, “Modeling of Organic Waste Classification as Raw Materials for Briquettes using Machine Learning Approach,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 3, pp. 577–585, 2023, doi: 10.14569/IJACSA.2023.0140367.
E. H. Sanchez, “Read, look and detect: Bounding box annotation from image-caption pairs,” IRT Saint Exupéry, no. Vl, 2023, [Online]. Available: http://arxiv.org/abs/2306.06149
M. Yaseen, “What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector,” 2024, [Online]. Available: http://arxiv.org/abs/2408.15857
C.-Y. Wang, I.-H. Yeh, and H.-Y. M. Liao, “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,” Tsinghua Univ., 2024, doi: 10.1007/978-3-031-72751-1_1.
J. Terven, D. M. Cordova-Esparza, A. Ramirez-Pedraza, E. A. Chavez-Urbiola, and J. A. Romero-Gonzalez, “Loss Functions and Metrics in Deep Learning,” pp. 1–53, 2023, [Online]. Available: http://arxiv.org/abs/2307.02694
J. Zophie and H. H. Triharminto, “Implemetasi Algoritma You Only Look Once ( YOLO ) menggunakan Web Camera untuk Mendeteksi Objek Statis dan Dinamis Implementation of You Only Look Once ( YOLO ) Algorithm using Web Camera for Static dan Dinamic Object Detection,” J. TNI Angkatan Udar., vol. 1, no. 1, pp. 98–109, 2020.
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