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PENGEMBANGAN SISTEM SORTIR OTOMATIS BUAH MENGGUNAKAN MODEL YOLOV8

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Penilaian

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This research aims to develop an automated sorting system for "achacha" fruits using a combination of Basler cameras, the YOLOv8 model for object detection, and the Epson T3 robot arm integrated through LabVIEW. The system is designed to classify fruits into "good" and "bad" categories based on visual characteristics, while ensuring the picking and transferring processes are automatic and precise. This system is expected to improve the efficiency and accuracy of small-scale fruit sorting.The research stages include image capture using a Basler camera operated via Pylon Viewer, dataset annotation on Roboflow, and training of the YOLOv8 model through Anaconda Prompt and Spyder. The resulting dataset is processed to train the YOLOv8 model, which outputs coordinates and fruit classifications. This data is then used by the robot arm to perform sorting based on the categories.The test results show that the system successfully achieved an accuracy of 88.1%, precision of 91.8%, recall of 91.8%, and F1-score of 91.8%. The Precision-Confidence Curve, Recall-Confidence Curve, and F1-Score Curve indicate reliable model performance, with an optimal confidence threshold of 0.217. Additionally, LabVIEW integration ensures smooth communication between the camera, computer, and robot, allowing real-time detection of fruit locations and precise placement to the appropriate locations.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000740	T164805	T1648052024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1648052024

Publisher

Indralaya : Prodi Teknik Mesin, Fakultas Teknik Universitas Sriwijaya., 2024

Collation

xiv, 51 hlm.; ill.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

621.807

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik mesin

Specific Detail Info

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Statement of Responsibility

EM

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