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All Journal SMATIKA Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) Journal of Applied Community Engagement (JACE) ABDIMASNU: Jurnal Pengabdian kepada Masyarakat Surya : Jurnal Pengabdian Kepada Masyarakat Smatika Jurnal : STIKI Informatika Jurnal
Dodit Suprianto
Jurusan Teknik Informatika, Politeknik Negeri Malang
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AIoT-enabled automated waste classification and real-time capacity monitoring system for smart waste management Ginanjar Suwasono Adi; Dodit Suprianto; Atik Novianti; Khurnia Fiddiana Rohmah; Elka Faizal; Mochammad Junus; Riona Ihsan Media
JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) Vol. 6 No. 1: March 2026
Publisher : Jurusan Teknik Elektro, Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/jitel.v6.i1.2026.23-34

Abstract

Population growth and urbanization have increased waste generation and intensified global waste management challenges. In Indonesia, national waste generation reached 38.4 million tons in 2023, with 38.38% remaining inadequately managed. This study aims to develop an Artificial Intelligence of Things (AIoT)-based system for automatic waste classification and real-time bin capacity monitoring. The system integrates the YOLOv8n model to identify four waste categories (organic, inorganic, hazardous/B3, and others) with ultrasonic sensors for capacity measurement, coupled with a web platform for data visualization. Model evaluation yielded a Macro F1-Score of 63.9%, with the best performance in the organic class (91.33%), followed by inorganic (68.37%), and hazardous/B3 (31.92%). Ultrasonic sensors demonstrated a near-linear relationship between waste height and capacity percentage (4.5% per cm), validating their reliability for real-time monitoring. The developed system proves the feasibility of AIoT integration for automated waste sorting, although further optimization is required to improve classification accuracy for minority classes. This research contributes to the development of intelligent solutions supporting more efficient and sustainable urban waste management.
Co-Authors Adi, Ginanjar Suwasono Agustina, Rini Atik Novianti Elka Faizal Elok Nur Hamdana Ikhwanul Muslimin Indra Dharma Wijaya Khurnia Fiddiana Rohmah M. Afif Hendrawan Mochammad Junus Pramana Yoga Saputra Rini Agustina Riona Ihsan Media Sovia Rosalin Tiffany Azhar Izzuddin Yoyok Seby Dwanoko