Deteksi Otomatis Terhadap Pelanggaran Pembuang Sampah Menggunakan Metode You Only Look Once (YOLO)
AbstractDisposing of garbage is a bad thing that can spoil the view, cause bad smells, cause low to high level flooding, cause various diseases and can pollute the environment. Even though the ban on disposing of trash has been implemented, there are still many who violate it. The importance of avoiding this makes a study aimed at automatically detecting violations of waste disposal. The method used is YOLOv5, this method is an algorithm that can identify objects with high accuracy, besides that it can also carry out tracking processes in the form of bounding boxes for objects in real time. The programming language used is Google Colaboratory. The dataset used is in the form of 800 images and 2 videos. After testing the results of the research using the You only look once (YOLO) method, the best results were obtained on the parameter batch 5 epochs 5 with an accuracy of 95%. From these results it can be concluded that the use of the YOLO method is very accurate when applied to the detection process of an object. |
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References
Puspitasari RL, Sugoro I, Elfidasari D, Perdana AT. 2018. Pengabdian Kepada Masyarakat Pelatihan Daur Ulang Sampah pada Siswa Sekolah Dasar di SDN 03 Cempaka Putih, Ciputat, Tangerang Selatan. J Al-AZHAR Indones SERI SAINS DAN Teknol. 4(2):91.
Zambana FL. 2019. Strategi Adaptasi Masyarakat Terhadap Sampah Limbah Rumah Tangga Dengan Mengaplikasikan 3r (Recycle, Reuse, Dan Reduce) Di Desa Jerowaru. 1(1):99–105.
Sari N, Mulasari SA. 2017. Pengetahuan, Sikap Dan Pendidikan Dengan Perilaku Pengelolaan Sampah Di Kelurahan Bener Kecamatan Tegalrejo Yogyakarta. J Med Respati. 12(April):1907–3887
Muchsin T. 2017. Peran Pemerintah Desa dalam Pengelolaan Sampah Perspektif Peraturan Daerah Nomor 2 Tahun 2015 Tentang Pengelolaan Sampah. 05(04):72–90
Patras MD, Mahihodi AJ. 2018. Faktor-Faktor Yang Berhubungan Dengan Perilaku Masyarakat Dalam Membuang Sampah Di Tepi Pantai Kelurahan Kolongan Akembawi Kecamatan Tahuna Barat. J Ilm Sesebanua. 2(21):57–62.
Dewi, Syarifah Rosita. (2018). Deep Learning Object Detection pada Video menggunakan Tensorflow dan Convolutional Neural Network. Yogyakarta.
Choldun, M. I., & Surendro, K. (2018). Klasifikasi Penelitian Dalam Deep Learning. Improve, 10(1), 25-33.
Kelvin, K., & Suprapto, B. Y. (2019). Sistem Klasifikasi Sampah Berbasis Convolutional Neural Network (Doctoral dissertation, Sriwijaya University).
Abidin, Z. (2021). TA: Klasifikasi Jenis Kendaraan pada Gerbang Tol Menggunakan Metode You Only Look Once (YOLO) (Doctoral dissertation, Universitas Dinamika).
Honainah. 2022. Penerapan Metode Faster Region Convolutional Neural Network (Faster R-CNN) untuk Deteksi Otomatis Interaksi Laki-laki dan Perempuan. NJCA (Nusantara Journal of Computers and Its Application). 7;1: 9-18.
Darmawan, D. (2021). Deteksi Masker Melalui Video CCTV Menggunakan You Only Look Once (Doctoral dissertation, STMIK Global Informatika Mdp).
Putra, B., Nugroho, B., & Anggraeny, F. (2021). Penggunaan lift pada gedung-gedung Deteksi dan Menghitung Manusia Menggunakan YOLO-CNN. Jurnal Informatika dan Sistem Informasi (JIFoSI), 2(1), 67-76.
JIWOONG, C., DAYOUNG, C., HYUN, K. & LEE, H.-J., 2019. Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving. Seoul, IEEE International Conference on Computer Vision.
FANG, W., WANG, L. & REN, P., 2020. Tinier-YOLO: A Real-Time Object DetectionMethod for Constrained Environments. IEEE Access, Volume 8, pp. 1935 – 1944
ADARSH, P., RATHI, P. & KUMAR, M., 2020. YOLO v3-Tiny: Object Detection and Recognition using one stage improved model. Coimbatore, s.n.
10.33650/trilogi.v4i2.6676 |
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