The Clustering Rice Plant Diseases Using Fuzzy C-Means and Genetic
Algorithm
Dublin Core
Title
The Clustering Rice Plant Diseases Using Fuzzy C-Means and Genetic
Algorithm
Algorithm
Subject
Clustering, Fuzzy C-Means, Genetic Algorithm, Image Processing, Rice Plants Diseases
Description
Rice is an agricultural sector that is very important for Indonesia's economy. The main problem with rice plants is pest and
disease control which has a very dangerous impact as well as economic losses for farmers. The characteristics that are very
visible on rice leaves have a greater area than other plant structures, rice leaves can be applied for early diagnosis of rice
plant diseases. Fuzzy C-Means (FCM) and Genetic Algorithm-Fuzzy C-Means are the approaches employed (GA-FCM). The
center of the cluster is obtained while adopting genetic algorithms for optimization. The primary dataset used in this research
is Teaching Sawah Farm IPB, and the secondary dataset is UCI Rice Leaf Diseases. According to the results of the comparison
the GA-FCM optimization results in a higher level of clustering precision with a 65% optimal cluster center point on the
silhoutte coefficient value compared to just 60% for FCM. This research shows the results that the proposed method can add
5% accuracy to the clustering results in terms of identifying the types of rice plant diseases properly
disease control which has a very dangerous impact as well as economic losses for farmers. The characteristics that are very
visible on rice leaves have a greater area than other plant structures, rice leaves can be applied for early diagnosis of rice
plant diseases. Fuzzy C-Means (FCM) and Genetic Algorithm-Fuzzy C-Means are the approaches employed (GA-FCM). The
center of the cluster is obtained while adopting genetic algorithms for optimization. The primary dataset used in this research
is Teaching Sawah Farm IPB, and the secondary dataset is UCI Rice Leaf Diseases. According to the results of the comparison
the GA-FCM optimization results in a higher level of clustering precision with a 65% optimal cluster center point on the
silhoutte coefficient value compared to just 60% for FCM. This research shows the results that the proposed method can add
5% accuracy to the clustering results in terms of identifying the types of rice plant diseases properly
Creator
Faza Adhzima1
, Yandra Arkeman2
, Irman Hermadi
, Yandra Arkeman2
, Irman Hermadi
Publisher
IPB University
Date
20-04-2022
Contributor
Fajar agus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Faza Adhzima1
, Yandra Arkeman2
, Irman Hermadi, “The Clustering Rice Plant Diseases Using Fuzzy C-Means and Genetic
Algorithm,” Repository Horizon University Indonesia, accessed June 4, 2025, https://repository.horizon.ac.id/items/show/9147.
Algorithm,” Repository Horizon University Indonesia, accessed June 4, 2025, https://repository.horizon.ac.id/items/show/9147.