Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Identifikasi Penyakit pada Tanaman Jeruk Berdasarkan Citra Daun

Abiyan Naufal Hilmi; Eva Yulia Puspaningrum; Henni Endah Wahanani

doi:10.62951/router.v2i2.78

Authors

Abiyan Naufal Hilmi Universitas Pembangunan Nasional “Veteran” Jawa Timur
Eva Yulia Puspaningrum Universitas Pembangunan Nasional “Veteran” Jawa Timur
Henni Endah Wahanani Universitas Pembangunan Nasional “Veteran” Jawa Timur

DOI:

https://doi.org/10.62951/router.v2i2.78

Keywords:

Image Management, K-Nearnest Neighbor (KNN), Disease Identification, Citrus Plants, Accuracy

Abstract

The development of image processing technology today can create systems that are able to effectively recognize digital images, one of which is in the field of agriculture for plant disease identification. Citrus plants experience a decrease in productivity due to pathogen attacks on leaves such as Black Spot, Cancer, and CVDP so that disease identification is needed. The classification method that can be used to classify images is the K-Nearest Neighbor (K-NN) algorithm because it is simple and has high accuracy in image management. This study aims to implement and determine the performance of the K-NN algorithm in identifying citrus plant diseases based on leaf images. This research uses a dataset from the Kaggle website of 1,096 images. There are 12 research scenarios using the comparison between test data and training data as much as 4, namely (90% training data + 10% test data, 80% training data + 20% test data, 70% training data + 30% test data, 60% training data + 40% test data) and testing with 3 random state values (42, 32, 22). The results showed that the K-NN algorithm is very effective in identifying citrus plant diseases with the highest accuracy value in the 90% training data scenario and 10% test data with a value of K = 2 which is 98.5%.

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