An IoT-Based Intelligent Waste Segregation System with Real-Time Capacity Monitoring

Authors

  • I Gede Wiryawan Politeknik Negeri Jember
  • Muhammad Nauval Hamdhani Politeknik Negeri Jember
  • Mohammad Abdul Azis Politeknik Negeri Jember
  • Muhammad Lukmanul Hakim Politeknik Negeri Jember
  • Achmad Sofyan Hakiki Politeknik Negeri Jember
  • M. Is’adul Ikhwan Politeknik Negeri Jember
  • Tiara Agustina Putri Wulandari Politeknik Negeri Jember
  • Nency Elvaretta Ardelia Politeknik Negeri Jember

DOI:

https://doi.org/10.31937/ijnmt.v13i1.4743

Abstract

Waste management in Indonesia is confronting a critical crisis, marked by the accumulation of 85% of waste in nearly full landfills and persistently low rates of source segregation, particularly in university campus environments. This study presents the development of RecyClean Smart Bin, a prototype IoT-based intelligent waste bin that integrates an ultrasonic sensor for real-time bin capacity monitoring, proximity and capacitive sensors for automatic classification of organic, inorganic, and metallic waste, and a web-based application for remote monitoring. The methodology encompasses hardware–software co-design, firmware programming, system implementation, and functional validation conducted over five days using fifteen representative waste samples. Results indicate excellent ultrasonic sensor accuracy (maximum deviation of 0.6 cm) and an overall sorting success rate of 80% (12 out of 15 samples), with particularly high precision in metallic waste detection. The proposed system offers a practical contribution toward advancing the circular economy and enabling sustainable smart environments.

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Published

2026-06-30

How to Cite

I Gede Wiryawan, Muhammad Nauval Hamdhani, Mohammad Abdul Azis, Muhammad Lukmanul Hakim, Achmad Sofyan Hakiki, M. Is’adul Ikhwan, … Nency Elvaretta Ardelia. (2026). An IoT-Based Intelligent Waste Segregation System with Real-Time Capacity Monitoring. IJNMT (International Journal of New Media Technology), 13(1), 83–90. https://doi.org/10.31937/ijnmt.v13i1.4743