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Identifikasi Penyakit pada Daun Pohon Pinang Menggunakan Pengolahan Citra Digital

Rafli, Muhammad (2023) Identifikasi Penyakit pada Daun Pohon Pinang Menggunakan Pengolahan Citra Digital. Other thesis, Universitas Islam Riau.

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Abstract

Areca tree plantations are common in Indragiri Hilir (INHIL) district. Many areca nut farmers in the city of Tembilahan have failed to harvest areca fruit because they were attacked by pests, especially on the leaves of the areca nut tree. Identification of diseases on areca palm leaves with digital image processing can be done to identify the types of diseases on areca palm leaves consisting of red rust, yellow leaves and yellow leaf spots. Identification of diseases on areca palm leaves with digital image processing starts from the image acquisition stage using a device to take images of areca palm leaves. Then in the pre-image processing stage, the areca tree leaf image data is processed using the Resize, Gaussian Blur and Mean Shift Filtering methods. The segmentation stage uses the HSV (Hue, Saturation and Value) color space method and the Active Contour segmentation method. Classification using the K-Nearest Neighbor (KNN) algorithm is a method for identifying the types of diseases on areca palm leaves. The results of the system in identifying the image data of areca tree leaves produce an accuracy of 70%. From the process of identifying diseases on areca leaf leaves, it can be concluded that the process of identification system for areca leaf diseases can easily identify and differentiate the types of diseases on areca palm leaves.

Item Type: Thesis (Other)
Contributors:
Contribution
Contributors
NIDN/NIDK
Thesis advisor
Yulianti, Ana
1024077901
Uncontrolled Keywords: Active Contour, K Nearest Neighbord (KNN), digital image, image processing
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Divisions: > Teknik Informatika
Depositing User: Uthi kurnia S.IP
Date Deposited: 18 Jun 2026 08:19
Last Modified: 18 Jun 2026 08:19
URI: https://repository.uir.ac.id/id/eprint/32736

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