KLASIFIKASI KESEGARAN WORTEL MENGGUNAKAN INCEPTIONV3

AL FARIZI, PUTRA and Palupi Rini, Dian (2024) KLASIFIKASI KESEGARAN WORTEL MENGGUNAKAN INCEPTIONV3. Undergraduate thesis, Sriwijaya University.

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Abstract

Carrot, scientifically known as Daucus carota L., is a root vegetable that is widely consumed around the world. Visual determination of carrot freshness visual determination by humans can be subjective and not always efficient due to the differences in viewpoints. The InceptionV3 model, which is one of the architectural models of CNN gets good results in classifying image cases. in classifying image cases, so the InceptionV3 model will be developed for carrot freshness classification. developed for carrot freshness classification. This model aims to identify whether a carrot can be considered fresh or rotten based on its visual image. Thus, this research can obtain good results to detect the Carrot Freshness and help in ensuring safe and high quality products to consumers in the agriculture and food industry. to consumers in the agriculture and food industry. The results showed model with 20 epochs, RMSprop optimizers, learning rate of 0.00001, dense with 128 neurons, Relu activation function, and dropout of 0.2, has successfully achieved an accuracy value of 94.17%, precision of 94.74%, sensitivity of 93.10%, and f1-score of 93.91%. It can be concluded that the classification of carrot freshness using the InceptionV3 model shows excellent very good performance.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	InceptionV3, Wortel, Kesegaran Wortel, Klasifikasi
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Putra Al Farizi
Date Deposited:	21 May 2024 06:00
Last Modified:	21 May 2024 06:00
URI:	http://repository.unsri.ac.id/id/eprint/144764

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