Skripsi

KLASIFIKASI CITRA JENIS BUAH DAN SAYURAN MENGGUNAKAN ALGORITMA YOLOV11

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Penilaian

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Automatic image classification of fruits and vegetables plays a crucial role in enhancing efficiency in the agricultural and retail sectors, yet it faces challenges due to visual complexities such as intra-class variation and inter-class similarity. This research aims to implement and evaluate the effectiveness of the You Only Look Once version 11 (YOLOv11) algorithm, specifically the YOLOv11s-cls variant, for the image classification task of 34 types of fruits and vegetables. The dataset used is "Fruit and Vegetable Classification" from Kaggle, which has undergone a cleaning process, resulting in approximately 2913 images. Pre-processing methods include resizing images to 640x640 pixels and data augmentation through stretching with a 1:1 aspect ratio. The YOLOv11s-cls model, previously trained on ImageNet, was fine-tuned using a transfer learning approach. Training was conducted for 20 epochs with monitoring of loss and accuracy curves. Model performance evaluation utilized metrics such as accuracy, precision, recall, F1-score, and a confusion matrix. The research results indicate that the YOLOv11s-cls model achieved an overall accuracy of 90,8% on the test set. Analysis of the confusion matrix and per-class F1-scores identified classes with excellent performance (e.g., cauliflower, corn, cucumber with an F1-score of 1,00) as well as more challenging classes (e.g., chilli pepper with an F1-score of 0,58, potato with 0,67, apple and paprika with 0,71), generally attributed to visual similarities between classes. This study demonstrates that YOLOv11s-cls is a promising algorithm for fruit and vegetable image classification, contributing to the development of automatic identification systems in related fields.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005378	T183045	T1830452025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1830452025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 125 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika
Buah dan Sayuran

Specific Detail Info

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Statement of Responsibility

KA

Other version/related

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