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All Journal Jiko (Jurnal Informatika dan komputer) Buana Information Technology and Computer Sciences (BIT and CS) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Scientific Student Journal for Information, Technology and Science
Tatang Rohana
Universitas Buana Perjuangan Karawang
Computer Science & IT

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DETECTION OF THE SIZE OF PLASTIC MINERAL WATER BOTTLE WASTE USING THE YOLOV5 METHOD Dony Dwi Karyanto; Jamaludin Indra; Adi Rizky Pratama; Tatang Rohana
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8535

Abstract

The use of plastic bottles for various needs is increasingly massive, especially in consumption needs such as mineral water bottles. The use of plastic bottles is used to reduce costs and be effective in maintaining the quality of mineral water, but its impact can affect natural conditions if not managed properly. Plastic bottle waste if left buried in the ground will have difficulty expanding, which can cause environmental pollution. Therefore, we can take advantage of technology to sort plastic bottle waste using a camera based on the size of plastic bottles. Differentiating the size of bottles aims to distinguish the economic value when exchanged at the waste bank. This technology utilizes object detection and recognition functions such as the YOLO (You Only Look Once) method. YOLO is a detection method that is a development of the CNN (Convolutional Neural Network) algorithm. By using YOLOv5, we can detect objects in the form of plastic bottle waste of various different sizes. To maximize object detection according to size, data annotation is done by creating a Bounding Box on each dataset according to its size. The test was carried out with several different distance configurations including 40cm, 80cm and 1m. Detection results using YOLOv5 produce up to 84% accuracy in real-time.
Co-Authors Adi Rizky Pratama Aditya Zatnika Andika Putra Anis Fitri Nur Masruriyah Ayu Juwita Bayu Biantara Dony Dwi Karyanto Dwi Kusumaningrum Euis Nurlaelasari Fadzly Maulana Hidayat Indra, Jamaludin Kiki Baihaqi Kusnantoro Kusnantoro Mudzakir, Tohirin Al Priyatna, Bayu Rahmad Aditiya Santi Lestari Setiyo Eko Budiyono Suningwar Mujiana Syamsul Ma’arif