Implementation of YOLO v11 for Image-Based Litter Detection and Classification in Environmental Management Efforts
This research implements YOLO v11 for image-based waste detection and classification to improve waste management efficiency. The model recognizes four categories of waste: inorganic, organic, hazardous and residual. The training results showa mAP@0.5 of 0.989 and a maximum F1 of 0.98 at an optimal c...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-06-01
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| Series: | Journal of Applied Informatics and Computing |
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| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9213 |
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| _version_ | 1850080125579689984 |
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| author | Lingga Kurnia Ramadhani Bajeng Nurul Widyaningrum |
| author_facet | Lingga Kurnia Ramadhani Bajeng Nurul Widyaningrum |
| author_sort | Lingga Kurnia Ramadhani |
| collection | DOAJ |
| description | This research implements YOLO v11 for image-based waste detection and classification to improve waste management efficiency. The model recognizes four categories of waste: inorganic, organic, hazardous and residual. The training results showa mAP@0.5 of 0.989 and a maximum F1 of 0.98 at an optimal confidence level of 0.669. The model had high precision on the Organic (0.995) and B3 (0.991) classes, but faced difficulties in classifying the Residue category. The confusion matrix revealed most of the predictions were accurate, despite some misclassification. The model also showed stable performance under various lighting and background conditions. With this reliability, YOLO v11 can be applied in automated sorting systems to improve recycling efficiency and support sustainable environmental management, although further improvements to data augmentation and class weight adjustment are still needed. |
| format | Article |
| id | doaj-art-633de21b1129400aa4d4a5046fee1fad |
| institution | DOAJ |
| issn | 2548-6861 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-633de21b1129400aa4d4a5046fee1fad2025-08-20T02:45:02ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-06-019361762410.30871/jaic.v9i3.92136758Implementation of YOLO v11 for Image-Based Litter Detection and Classification in Environmental Management EffortsLingga Kurnia Ramadhani0Bajeng Nurul Widyaningrum1Universitas IVET SemarangPoliteknik Bina Trada SemarangThis research implements YOLO v11 for image-based waste detection and classification to improve waste management efficiency. The model recognizes four categories of waste: inorganic, organic, hazardous and residual. The training results showa mAP@0.5 of 0.989 and a maximum F1 of 0.98 at an optimal confidence level of 0.669. The model had high precision on the Organic (0.995) and B3 (0.991) classes, but faced difficulties in classifying the Residue category. The confusion matrix revealed most of the predictions were accurate, despite some misclassification. The model also showed stable performance under various lighting and background conditions. With this reliability, YOLO v11 can be applied in automated sorting systems to improve recycling efficiency and support sustainable environmental management, although further improvements to data augmentation and class weight adjustment are still needed.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9213yolo v11object detectionjunkapps |
| spellingShingle | Lingga Kurnia Ramadhani Bajeng Nurul Widyaningrum Implementation of YOLO v11 for Image-Based Litter Detection and Classification in Environmental Management Efforts Journal of Applied Informatics and Computing yolo v11 object detection junk apps |
| title | Implementation of YOLO v11 for Image-Based Litter Detection and Classification in Environmental Management Efforts |
| title_full | Implementation of YOLO v11 for Image-Based Litter Detection and Classification in Environmental Management Efforts |
| title_fullStr | Implementation of YOLO v11 for Image-Based Litter Detection and Classification in Environmental Management Efforts |
| title_full_unstemmed | Implementation of YOLO v11 for Image-Based Litter Detection and Classification in Environmental Management Efforts |
| title_short | Implementation of YOLO v11 for Image-Based Litter Detection and Classification in Environmental Management Efforts |
| title_sort | implementation of yolo v11 for image based litter detection and classification in environmental management efforts |
| topic | yolo v11 object detection junk apps |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9213 |
| work_keys_str_mv | AT linggakurniaramadhani implementationofyolov11forimagebasedlitterdetectionandclassificationinenvironmentalmanagementefforts AT bajengnurulwidyaningrum implementationofyolov11forimagebasedlitterdetectionandclassificationinenvironmentalmanagementefforts |