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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Buana Informatika Bulletin of Electrical Engineering and Informatics Media Aplikom Kalbiscientia Jurnal Sains dan Teknologi ABDIMAS Jurnal Pengabdian Kepada Masyarakat ABDINE Jurnal Pengabdian Masyarakat KALBISIANA Jurnal Sains, Bisnis dan Teknologi
Yulius Denny Prabowo
Institut Teknologi dan Bisnis Kalbis
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

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Low resource deep learning to detect waste intensity in the river flow Ferdinandus Fidel Putra; Yulius Denny Prabowo
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3062

Abstract

Waste has become a significant problem in Indonesia, especially in the capital city of Jakarta due to many disasters caused by it. The one cause of flooding is the blockage of river flow by waste. The monitoring of litter is essential to find out the waste intensity in the river. The research was formed which aims to produce an application that can detect, track, and calculate river waste using YOLO v3 algorithm. This research was done in order to simplify the process of monitoring waste in the river and can calculate waste using video. This research uses 340 images directly from photos and videos, captured by researchers-detection of waste processed frame by frame by changing video into several structures. From the acquired result from the experiment, it's proven that YOLO v3 can be used for detection and counting waste recorded on video. The result of this research is an application that can detect waste and it is able to detect said objects with 98.74% of confidence from case video.
Co-Authors Alvin Andreas Anil Kumar Siliwangi Bima Dirgantara Bima Dirgantara Ester Lumba Ferdinandus Fidel Putra Harya Bima Dirgantara Hengky Krisna Fernando Meylisa Suryawiguna Muhammad Matrix Indra Nawawi Daeng Parani Nathanael Rezaputra Paramaresthi Windriyani Rangga Eka Nanda Richard Sigit Gunawan Tedi Lesmana Marselino Willyam Saputra Yulia Ery Kurniawati