…
comment: The repository administrator has not yet configured an RDF license.
primary topic: PEMODELAN KLASIFIKASI PENYAKIT PADA DAUN ANGGUR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)

PEMODELAN KLASIFIKASI PENYAKIT PADA DAUN ANGGUR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)
type: Thesis
type: Article
abstract: PEMODELAN KLASIFIKASI PENYAKIT PADA DAUN ANGGUR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) Novia Permata Sari1 ; Sufiatul Maryana2 ; Adriana Sari Aryani3 . Program Studi Ilmu Komputer1,2,3 Universitas Pakuan, Bogor, Indonesia1,2,3 www.unpak.ac.id1,2,3 Nopiapermatasar518@gmail.com1 , sufiatul.maryana@unpak.ac.id2 , adriana.aryani@unpak.ac.id3 (*) Corresponding Author The creation is distributed under the Creative Commons Attribution-NonCommercial 4.0 International Lincense. Abstract- Grapevine diseases often pose a major challenge, leading to reduced crop quality and yield if not detected early. Manual disease identification typically requires specialized expertise, high precision, and considerable time. This study aims to develop an automated deep learning-based classification model for grapevine leaf diseases using the Convolutional Neural Network (CNN) method with a DenseNet121 base architecture. The dataset consists of 1,600 digital images of green grapevine leaves (*Vitis vinifera* L. var. Thompson Seedless) collected from plantations in Sukaraja District, Bogor Regency. These images are evenly distributed across four condition classes: Healthy, Black Measles (Esca), Leaf Blight (Isariopsis Leaf Spot), and Black Rot. Model testing was conducted using two data split scenarios: 80:10:10 and 70:15:15. The results demonstrate that the DenseNet121 architecture effectively extracts visual image characteristics. The best performance was achieved with the 70:15:15 data split, yielding a validation accuracy of 91.25%, whereas the 80:10:10 split resulted in an accuracy of 88.75%. The Healthy and Leaf Blight classes were identified with 100% accuracy. This classification model was integrated into a web application built on the Flask framework as a practical implementation to support smart a validation accuracy of 91.25%, whereas the 80:10:10 split resulted in an accuracy of 88.75%. The Healthy and Leaf Blight classes were identified with 100% accuracy. This classificat…
date: 2026-06-21
status: published
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
subject: Ilmu Komputer

Universitas Pakuan
type: Organization

Fakultas Matematika dan Pengetahuan Alam, Universitas Pakuan
type: Organization

Adriana Sari Aryani
type: Person
given name: Adriana Sari
family name: Aryani

Sufiatul Maryana
type: Person
given name: Sufiatul
family name: Maryana

Novia Permata Sari
type: Person
given name: Novia Permata
family name: Sari

Fakultas Matematika dan Ilmu Pnegetahuan Alam
type: Organization

Program Studi Ilmu Komputer
type: Organization

HTML Summary of #11061 PEMODELAN KLASIFIKASI PENYAKIT PADA DAUN ANGGUR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)
format: text/html
primary topic: PEMODELAN KLASIFIKASI PENYAKIT PADA DAUN ANGGUR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)

Ilmu Komputer
type: Concept