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All Journal Journal of System and Computer Engineering
Muhammad Nur
Institut Teknologi Bacharuddin Jusuf Habibie
Computer Science & IT Decision Sciences, Operations Research & Management

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Deteksi dan Estimasi Berat Sampah Plastik Berbasis Visi Komputer Andri Dwi Utomo; Mar’atuttahirah Mar’atuttahirah; A. Inayah Auliyah; Muhammad Nur
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2755

Abstract

Plastic waste management is an increasingly critical environmental challenge due to the growing volume of waste and the limitations of conventional manual weighing methods, which are inefficient and prone to human error. This study proposes a computer vision-based system for automatic detection, classification, and weight estimation of plastic waste using the YOLOv8n model for object detection and Random Forest Regression for weight estimation. The YOLOv8n model is used to detect and classify seven types of plastic waste based on the Resin Identification Code (RIC), namely PET, HDPE, PVC, LDPE, PP, PS, and OTHER. Subsequently, weight estimation is performed using a Random Forest Regression model based on bounding box features, including width, height, area, aspect ratio, and perimeter. The proposed system is evaluated using an unseen test set to ensure unbiased performance measurement. Experimental results show that the YOLOv8n model achieves a mean Average Precision (mAP@0.5) of 91.93% and mAP@0.5:0.95 of 73.27%, while the Random Forest Regression model achieves an R² score of 95.5% with a Mean Absolute Error (MAE) of 4.28 grams. These results demonstrate that the integration of object detection and regression enables accurate and automatic estimation of plastic waste weight, thereby improving the efficiency and objectivity of waste management systems.
Co-Authors A. Inayah Auliyah Andri Dwi Utomo Mar’atuttahirah Mar’atuttahirah