Skripsi

KLASIFIKASI VARIETAS BERAS MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR MOBILENETV2

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Penilaian

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Rice consists of various varieties withdistinctmorphological characteristics, yet manual classification remains subjective and reliant on expert judgment. This study develops an automated rice variety classification system using the Convolutional Neural Network (CNN) method with the MobileNetV2 architecture. The dataset includes 75,000 images of five rice varieties, divided into 70% training, 5% validation, and 25% testing. The model was trained using transfer learning, data augmentation, and callbacks such as ModelCheckpoint and EarlyStopping. The training results showed a validation accuracy of up to 99.09% with stable validation loss, indicating no overfitting. The model was evaluated using metrics such as accuracy, precision, recall, and F1-score, and implemented in a Streamlit-based application to provide an interactive classification interface. The results confirm that MobileNetV2 is an effective and efficient architecture for rice variety classification.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003409	T175757	T1757572025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1757572025

Publisher

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

Collation

xvi, 100 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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