CD Skripsi

Implementasi Deep Learning Untuk Deteksi Penyakit Pada Buah Jambu Biji Di Usia Dini Menggunakan Mobilenetv2

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ABSTRACT
Guava is one of the horticultural commodities with high economic value. However, its productivity may decline due to disease attacks that are difficult to detect during the early growth stage. This study applies a deep learning method with a transfer learning approach using the MobileNetV2 architecture to classify guava fruit conditions into two categories: healthy and diseased. The dataset consists of 548 images for training, 114 images for validation, and 114 images for testing. The model was successfully trained with optimal parameters and converted into TensorFlow Lite format to support deployment in an Android-based mobile application. The model achieved an accuracy of 94.02% on the validation set and 92.31% on the testing set, demonstrating strong generalization performance in early detection of guava fruit disease symptoms.
Keywords : Guava, Deep Learning, Transfer Learning, MobileNetV2

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2007113995

Penerbit

Pekanbaru : Universitas Riau – F.TEKNIK – INFORMATIKA., 2025

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2007113995

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNIK INFORMATIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

daus

Versi lain/terkait

Lampiran Berkas

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