Klasifikasi Penyakit Daun Tebu Menggunakan Algoritma Convolutional Neural Network (CNN) dan Support Vector Machine (SVM)

Yunizar, Sri Fatmawati (2024) Klasifikasi Penyakit Daun Tebu Menggunakan Algoritma Convolutional Neural Network (CNN) dan Support Vector Machine (SVM). Undergraduate thesis, Universitas Pembangunan Nasional "Veteran" Jawa Timur.

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Abstract

Indonesia is an agricultural country whose economy depends on the plantation sector, such as the sugarcane commodity in producing sugar. However, sugarcane productivity is often disrupted by disease attacks such as yellow disease, redrot, mosaic, and rust that make sugarcane productivity decrease. This disease attack must be detected immediately because it has an impact on reducing the quality and quantity of sugarcane to be harvested. However, the manual identification process is prone to human error and inefficient for large-scale plantations. Therefore, the utilization of machine learning technology to develop a sugarcane leaf disease classification model using Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches was conducted. This approach uses CNN as a feature extractor to obtain important characteristics of sugarcane leaf images, which are then classified by SVM. Through a series of trials, the results showed that the CNN and SVM models were able to provide a high accuracy of 90.32% with a computation time of 181.53 seconds in classifying sugarcane leaf diseases. The accuracy achieved by this model shows a good ability to identify and distinguish diseases in sugarcane leaves. The results of this study indicate that the CNN-SVM model has great potential in helping farmers to identify diseases in sugarcane plants.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Sari, Anggraini Puspita	NIDN0716088605	anggraini.puspita.if@upnjatim.ac.id
Thesis advisor	Aditiawan, Firza Prima	NIDN0023058605	firzaprima.if@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Sri Fatmawati Yunizar

Date Deposited:

20 Sep 2024 03:39

Last Modified:

20 Sep 2024 03:39

URI:

https://repository.upnjatim.ac.id/id/eprint/29200