CD Skripsi

Implementasi Deep Learning Untuk Klasifikasi Penyakit Pada Daun Kelapa Sawit Menggunakan Arsitektur Mobilenetv2

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ABSTRACT
Accurate and efficient identification of diseases in oil palm leaves is a crucial challenge in maintaining plantation productivity and preventing significant crop losses. Limited access to experts and slow detection in the field are often obstacles. This study aims to develop a palm oil leaf disease classification model using a deep learning approach based on Convolutional Neural Network (CNN) with MobileNetV2 architecture. This model utilizes a transfer learning strategy from pre-trained ImageNet weights and is optimized through a two-phase training strategy on a dataset consisting of 1200 augmented oil palm leaf images, covering four classes, namely Healthy Sample, Fusarium Wilt, Parlatoria Blanchardi, and Rachis Blight. Model testing results show an accuracy of 85% on separate test data. The MobileNetV2 architecture was chosen for its lightweight characteristics, making this model efficient and highly suitable for implementation on mobile devices to assist in rapid disease identification in the field and support decision-making by farmers.
Keywords: Deep Learning, Image Classification, MobileNetV2, Oil Palm Leaf Disease, Mobile Application

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2107112363

Penerbit

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

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2107112363

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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