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All Journal REKA ELKOMIKA Bulletin of Electrical Engineering and Informatics Jurnal Teknik Elektro Sinergi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JETT (Jurnal Elektro dan Telekomunikasi Terapan) JURNAL KAJIAN TEKNIK ELEKTRO Jurnal Tekno Insentif TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Indonesian Journal of Electrical Engineering and Computer Science REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat
LITA LIDYAWATI
Institut Teknologi Nasional (ITENAS) Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Real-time deep neural network-based waste detection and classification using a camera sensor Darlis, Arsyad Ramadhan; Lidyawati, Lita; Kristiana, Lisa; Hartati, Etih; Trisani, Faradilla Rizqi
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

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Abstract

Waste generation is a growing environmental concern, with manual sorting methods often being inefficient and error-prone, particularly under varying lighting and environmental conditions. In Indonesia, waste is typically categorized into organic and nonorganic, yet existing automated classification systems lack real-time capabilities and robustness in dynamic settings. This study proposes a novel real-time waste detection and classification system using a deep neural network, implemented on the Jetson Nano platform with a camera sensor. The system utilizes the ResNet-18 convolutional neural network architecture and is developed using Python. It is designed to distinguish between organic and nonorganic waste in real-time. Training was conducted over 30 epochs, and the system was tested under various lighting conditions—morning, daytime, afternoon, and nighttime. Results show high accuracy: 95.24% in the morning, 95.24% during the day, 90.45% in the afternoon, and 86.90% at night, with an average accuracy of 91.96%. Performance was influenced by factors such as lighting intensity, distance, waste position, changes in organic waste, and occlusion by plastic. The proposed system offers a significant improvement over traditional and existing methods by enabling accurate, real-time waste classification under diverse conditions, contributing to more efficient and intelligent waste management.
Co-Authors Adisty Hanny Asri Adisty Hanny Asri AGITA SYAIMI PUTRI UTAMI AKMAL, MULIA EZA Alfin Fernando Tamba ANITA PURNAMA SARI ARCHANDHIKA, HEGAR REFALDY Arif Fahrizal Arsyad Ramadhan Darlis ARSYAD RAMADHAN DARLIS BIMA BIMA, BIMA BUDI PRATAMA, BUDI DANI RUSIRAWAN DECY NATALIANA Deden Nur Rokhman DEWI, IRMA AMELIA DWI ARYANTA EDI YUSUF, EDI Fauziah, Dini FAZAR GUNTARA PRAJA GEMA ALFARISI DERI Hartati, Etih HEIFAT AHMAD ABDULLAH HUSAENI, DZIKRI FACHRI JAPE ATHAN BANGUN Kania Sawitri KRISTIANA, LISA Latif, Mochamad Fazri Abdul LUCIA JAMBOLA M.ICHWAN M.ICHWAN Muhammad Aluh Munawaroh, Sofa Jauharotul Narisha Kalya Mahsa NURUL WULANDARI PAULINE RAHMIATI, PAULINE pribadi, aditya arif RAISA MUKHTARETA Ramdhani, Teguh RATNA SUSANA Rea Ramada Jayandanu REGINA RIYANTIKA ROVAN NUGRAHA RUSTAMAJI RUSTAMAJI SABAT ANWARI Satria Ilma Romadoni Siswanto, Wahyu Dwi Trisani, Faradilla Rizqi TUTUN JUHANA Yanuary, Tio Hanif Yuli Sunarti ZUL RAMADHAN