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Vol. 11, No. 3, August 2026 (Article in Progress)

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AIoT-Enabled Automatic Waste Sorting System with Real-Time WhatsApp Notifications

https://doi.org/10.22219/kinetik.v11i3.2593
Muchamad Rusdan
Universitas Teknologi Bandung
Sri Kuswayati
Universitas Teknologi Bandung

Corresponding Author(s) : Muchamad Rusdan

muchamad.rusdan@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 3, August 2026 (Article in Progress)

Abstract

The waste management crisis, particularly in educational institutions, requires innovative solutions that combine artificial intelligence and automation. This research develops and evaluates an automated waste sorting system based on the Artificial Intelligence of Things (AIoT), integrated with WhatsApp notifications. The system utilizes the EfficientNet-B0 deep learning model optimized through transfer learning and runs on a Raspberry Pi 4 edge device to classify waste into five categories: plastic, paper, metal, glass, and organic, in real time. The classification results are translated into physical actions by a servo actuator mechanism, while ultrasonic sensors monitor the trash bin capacity. The real-time notification system, implemented through the WhatsApp API, sends alerts to administrators. A 30-day evaluation conducted on campus showed that the system achieved a classification accuracy of 92.3% with an inference latency of 1.8 seconds. The mechanical system successfully sorted waste with a 94.5% success rate, while WhatsApp notifications achieved a 99.1% delivery rate, with an average administrator response time of 8.2 minutes during operational hours. A comparative analysis demonstrated that the system increased sorting efficiency by 87% and reduced operational costs by 45% compared with manual waste sorting methods. These findings conclude that the proposed integration of edge AI, mechanical automation, and WhatsApp notifications provides a smart waste management solution that is not only effective and real-time but also practical, economical, and sustainable for wider implementation.

Keywords

Artificial Intelligence of Things Deep Learning Edge Computing Smart Waste Management WhatsApp Notifications Waste Classification
Rusdan, M., & Kuswayati, S. (2026). AIoT-Enabled Automatic Waste Sorting System with Real-Time WhatsApp Notifications. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(3), 493-502. https://doi.org/10.22219/kinetik.v11i3.2593
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