Klasifikasi Penyakit Tanaman Tebu dengan Pendekatan Support Vector Machine - All Iqbar Arifin; Muhammad Alditho Firlata; Aziz Arrasyid; Satria Andikah Putra (OKTAL : Jurnal Ilmu Komputer dan Sains, 2024)
This study proposes the use of the Support Vector Machine (SVM) method as an approach to classifying sugarcane plant diseases. This developing research requires an effective method to detect and classify sugarcane plant diseases so that treatment can be carried out appropriately. This study proposes the use of the Support Vector Machine (SVM) method as an approach to classifying sugarcane plant diseases. SVM was chosen because of its high ability to distinguish data from various classes even in complex dimensions, as well as its reliability in handling small datasets with a good level of accuracy. The data in this study were obtained from images of sugarcane leaves that had been classified into several disease categories. These images were then processed through a feature extraction process that included shape, texture, and color as the main parameters. The experimental results showed that the SVM approach could achieve a high level of accuracy in classifying sugarcane plant diseases. These findings indicate that SVM is an effective and efficient method for identifying diseases in sugarcane plants, and has the potential to be applied as a decision support system in sugarcane plantation management.