RobustMaturity Level Classification of Bell Pepper using CNN

RobustMaturity Level Classification of Bell Pepper using CNN

Ripeness, Classification, Bell Peppers, CNN, VGG16

The development of artificial intelligence has opened up new opportunities in various fields, including Bell Pepperimage detection. Theremaining issuesarethat the selection of the ripeness level of bell peppers manually can take a long time and requires more accuracy. The purpose of this research is to classify the maturity level of bell peppers and to determine the level of accuracy. The research method used isConvolutionalNeural Network (CNN) with tools or tools, namely Visual Studio Code in Python with TensorFlowFramework,as well as a pre-trained CNN architecturecalled VGG16. Bell peppers are divided into 3 levels of ripeness with different types of colors:green (unripe), yellow (half-ripe), and red (ripe). The results showed that in classifying the maturity level of bell peppers with an accuracy of 89%,precision 84%, recall of 83%, and F1-Score of 84%

Rino Aziz Fadhillah1, Irwansyah2

https://ijicom.respati.ac.id/index.php/ijicom/article/view/139/98

International Journal of Informatics and Computation (IJICOM)

2025

Fajar bagus W

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