Identification Types of Plastic Waste Based On The Yolo Method
Main Article Content
Abstract
Plastic is still widely used in the daily lives of Indonesian people. As well as being an inexpensive material, plastic is characterized by weather, lightweight and corrosion resistance. In Indonesia, awareness of waste is still very low, resulting in the indiscriminate accumulation of garbage. Some people don't know the material from the type of plastic used These issues can be solved by using artificial intelligence advancements, such as computer vision.The yolov3 Tiny method was used to identify the type of plastic. The types of plastic used in this study were PET, PP, and HDPE. This method is implemented in a desktop-based application. The system is successful with an average accuracy value of 53.3%. The system easily recognizes the PET type, with an accuracy rating 70% greater than the other varieties. Whereas for the PP type, the result is 60% and for the HDPE type, it gets a value of 30%. For researchers, in turn, it may add a greater number of data slices and vary to be even more accurate in their identification.
Downloads
Article Details
Copyright (c) 2024 Adi Kurniawan Saputro, Miftachul Ulum, Odiy Syahnurrokhim Khabibiy, Achmad Fiqi Ibadillah, Riza Alfita, Muttaqin Hardiwansyah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
F. R. Hendri and F. Utaminingrum, “Rancang Bangun Sistem Pengklasifikasi Jenis Sampah Organik dan Anorganik menggunakan metode You Only Look Once versi 3 berbasis Raspberry Pi,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 7, pp. 3509–3514, 2022.
L. J. J. Meijer, T. van Emmerik, R. van der Ent, C. Schmidt, and L. Lebreton, “More than 1000 rivers account for 80% of global riverine plastic emissions into the ocean,” Sci Adv, vol. 7, no. 18, Apr. 2021, doi: 10.1126/sciadv.aaz5803.
S. Suwarsih, M. I. Joesidawati, and S. Sriwulan, “Pelatihan Pemilahan Sampah Plastik Sebagai Bahan Biji Plastik Di Desa Palang,” JURNAL PENGABDIAN KEPADA MASYARAKAT, vol. 9, no. 2, p. 162, Dec. 2019, doi: 10.30999/jpkm.v9i2.693.
T. A. M. I. N. Pratama, T. Rohana, and T. Al Mudzakir, “Pengenalan Sampah Plastik Dengan Model Convolutional Neural Network,” in Conference on Innovation and Application of Science and Technology (CIASTECH 2020), 2020, pp. 691–698.
C. N. Liunanda, S. Rostianingsih, and A. N. Purbowo, “Implementasi Algoritma YOLO pada Aplikasi Pendeteksi Senjata Tajam di Android.,” Jurnal Infra, vol. 8, no. 2, pp. 235–241, 2020.
M. Basrowi, Manfaat Plastik. Alprin, 2020.
L. Liu, B. Zhou, G. Liu, D. Lian, and R. Zhang, “Yolo-Based Multi-Model Ensemble for Plastic Waste Detection Along Railway Lines,” in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Jul. 2022, pp. 7658–7661. doi: 10.1109/IGARSS46834.2022.9883308.
B. Putra, G. Pamungkas, B. Nugroho, and F. Anggraeny, “Deteksi dan Menghitung Manusia Menggunakan YOLO-CNN,” Jurnal Informatika dan Sistem Informasi, vol. 2, no. 1, pp. 67–76, 2021.
N. E. Budiyanta, M. Mulyadi, and H. Tanudjaja, “Sistem Deteksi Kemurnian Beras berbasis Computer Vision dengan Pendekatan Algoritma YOLO,” Jurnal Informatika: Jurnal pengembangan IT, vol. 6, no. 1, pp. 51–55, 2021.
S. Ma’arif, T. Rohana, and K. Baihaqi, “Deteksi Jenis Beras Menggunakan Algoritma YOLOv3,” Scientific Student Journal for Information, Technology and Science, vol. 3, no. 2, pp. 219–226, 2022.
M. C. Wujaya and L. W. Santoso, “Klasifikasi Pakaian Berdasarkan Gambar Menggunakan Metode YOLOv3 dan CNN,” Jurnal Infra, vol. 9, no. 1, pp. 103–109, 2021.
D. G. Arwindo, E. Y. Puspaningrum, and Y. V. Via, “Identifikasi Penggunaan Masker Menggunakan Algoritma CNN YOLOv3-Tiny,” Prosiding Seminar Nasional Informatika Bela Negara, vol. 1, pp. 153–159, Nov. 2020, doi: 10.33005/santika.v1i0.41.
A. S. Riyadi, I. P. Wardhani, M. S. Wulandari, and S. Widayati, “Perbandingan Metode ResNet, YoloV3, dan TinyYoloV3 pada Deteksi Citra dengan Pemrograman Python,” PETIR, vol. 15, no. 1, pp. 135–144, Jan. 2022, doi: 10.33322/petir.v15i1.1302.
P. Adarsh, P. Rathi, and M. Kumar, “YOLO v3-Tiny: Object Detection and Recognition using one stage improved model,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Mar. 2020, pp. 687–694. doi: 10.1109/ICACCS48705.2020.9074315.
M. R. Pratama, E. P. Bhayangkara, and J. M. Ishlah, “MODEL APLIKASI DOCUMENT SCANNER MENGGUNAKAN OPERATOR CANNY DAN CONTOUR PADA OPEN CV BERBASIS DESKTOP,” JUTEKIN (Jurnal Teknik Informatika), vol. 10, no. 2, Nov. 2022, doi: 10.51530/jutekin.v10i2.635.