Solar-Powered Organic Leaves Waste Shredder Machine with IoT-Based Monitoring Integration

  • Hafidz I
  • Wednestwo Samuel G
  • Jose Anto Kurniawan C
  • et al.
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Abstract

Organic waste management relying on conventional nonrenewable energy, which produces significant carbon emissions, poses challenges to environmental pollution. A shift to sustainable waste management practices prioritizing renewable energy sources and reducing greenhouse gas emissions is needed. This study aims to design and develop a prototype of an organic waste shredder powered by solar panels, integrating IoT technology to address the environmental impact of traditional shredding methods. The solar-powered shredder is portable and addresses challenges in processing organic waste, especially dry leaves, while promoting renewable energy use in composting. The portable machine operates with a 30-watt, 12-volt DC motor, achieving a maximum speed of 3500 RPM, and features a Blynk-based monitoring system for real-time control and data analysis. Key sensors, including the INA219 and load cell, demonstrate low error rates. The shredder processes various leaf types, with production capacities of 23.96 kg/hour for grape leaves, 20.18 kg/hour for ketapang leaves, 19.93 kg/hour for cherry leaves, and 21.6 kg/hour for mango leaves, achieving 93.09% and 95.17%. The portable power station fully charges in 15 hours and provides up to 18 hours of continuous power. The Blynk application monitors and controls the system within a 9-meter range before disconnection. By integrating solar energy and IoT technology, this device offers a sustainable alternative to traditional shredding methods, reducing carbon emissions and promoting environmentally friendly waste management practices.

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APA

Hafidz, I., Wednestwo Samuel, G., Jose Anto Kurniawan, C., Alfattah, H., & Dwi Putranto, R. (2025). Solar-Powered Organic Leaves Waste Shredder Machine with IoT-Based Monitoring Integration. Elinvo (Electronics, Informatics, and Vocational Education), 10(1), 30–43. https://doi.org/10.21831/elinvo.v10i1.82364

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