AIoT-Enabled Automatic Waste Sorting System with Real-Time WhatsApp Notifications - Muchamad Rusdan; Sri Kuswayati (Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2026)
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 Artificial Intelligence of Things (AIoT) integrated with WhatsApp notifications. The system utilizes the EfficientNet-B0 deep learning model optimized with 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. Classification results are translated into physical actions by a servo actuator mechanism, while ultrasonic sensors monitor trash bin capacity. The real-time notification system via WhatsApp API sends alerts to administrators. A 30-day evaluation on campus showed that the system achieved 92.3% classification accuracy with an inference latency of 1.8 seconds. The mechanical system successfully sorted waste with a 94.5% success rate, and WhatsApp notifications had a 99.1% delivery rate, with an average administrator response time of 8.2 minutes during operational hours. A comparative analysis demonstrated that this system increased sorting efficiency by 87% and reduced operational costs by 45% compared to manual waste sorting methods. These findings conclude that the proposed integration of edge AI, mechanics, and WhatsApp notifications creates a smart waste management solution that is not only effective and real-time but also practical, economical, and sustainable for wider implementation.