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The research aims to assess the effectiveness and economic viability of AI-driven robotic waste sorting in urban environments of Indonesia.

AI-Driven Robotic Waste Sorting for Techno-Economic Assessment in Urban Indonesia

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Demonstrates the economic feasibility of AI-driven robotic waste sorting in urban Indonesia, suggesting improved recycling efficiency.

The research aims to assess the effectiveness and economic viability of AI-driven robotic waste sorting in urban environments of Indonesia.
Design and evaluate an AI-driven robotic sorting system in Jakarta and South Tangerang.
Integrate YOLO based object detection and vision-guided robotic manipulation.
Conduct a techno-economic assessment considering local wage levels and recyclable market prices.
Measure sorting accuracy, material purity, and throughput in real operational conditions.
Achieved sorting accuracy of 90% and material purity of 95.1%.
Demonstrated throughput of 50 items per minute, exceeding manual sorting performance.
Estimated payback period for the system ranges from 4.3 to 4.9 years.

Nurhaida et al. (2026) studied this question.

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