Pendeteksian Anomali Pada Daun Tanaman Jambu Biji Menggunakan Isodata Cluster - Nadila Harianti; Yunita Sari; Mufida Khairani (Universitas Harapan Medan, 2025)
The purpose of this study is to develop a disease identification system for guava leaves using the ISODATA Clustering method. Diseases affecting guava plants may reduce plant quality and productivity; therefore, an automatic identification system is required. The ISODATA method was selected because it can automatically organize image data through cluster creation, merging, and splitting processes. The system was developed using Visual Studio and utilized guava leaf images as input data. The research stages included image acquisition, pixel extraction, clustering using ISODATA, and disease identification based on color pattern grouping. The results indicate that the ISODATA Clustering method is capable of grouping leaf color patterns and supporting the identification of guava leaf diseases. The developed system classifies leaves into several categories, including leaf blight, leaf spot, rust disease, and healthy leaves. This system is expected to assist farmers in early disease detection and decision-making for prevention and treatment.