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Elka Faizal
Jurusan Teknik Mesin, Politeknik Negeri Malang
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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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 Agus Dani Agus Hardjito Agus Harijono Agus Setiawan Alfi Tranggono Agus Salim Atik Novianti BAYU PRANOTO Dodit Suprianto Ginanjar Suwasono Adi Gumono Hangga Wicaksono Hilmi Iman Firmansyah Imam Mashudi Indrawan Nugrahanto, Indrawan Khurnia Fiddiana Rohmah Kris Witono Maskuri Maskuri Mochammad Junus Moh. Nasir Hariyanto Muhammad Fakhruddin Muzaki, Mochamad Nike Nur Farida Nila Alia Nurlia Pramita Sari Riona Ihsan Media Sarjiyana Sugeng Hadi Susilo Sulistyono Sulistyono Sungkono Sungkono Suyanta Suyanta Talifatim Machfuroh Yuniarto Agus Winoko Zahratul Jannah AR