IMPLEMENTASI ALGORITMA SUPPORT VECTOR MACHINE (SVM) UNTUK MENDETEKSI PENYAKIT PADA DAUN KELAPA SAWIT : IMPLEMENTATION OF SUPPORT VECTOR MACHINE (SVM) ALGORITHM FOR DETECTING DISEASES IN OIL PALM LEAVES - Bintang Ananta (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
This study aims to implement the Support Vector Machine (SVM) algorithm for classifying oil palm leaf conditions using digital image processing. Accurate and timely identification of leaf conditions is essential for maintaining seedling quality and supporting oil palm productivity. However, manual observation methods are often limited by subjectivity and differences in observer experience. The dataset used in this study consisted of oil palm leaf images representing three conditions: healthy leaves, yellow leaves, and spotted leaves. The research process included image preprocessing, color and texture feature extraction, and classification using the SVM algorithm. The dataset was divided into training and testing sets to evaluate the model's performance. The experimental results showed that the SVM model achieved an accuracy of 91.53% in classifying oil palm leaf conditions. These findings indicate that the SVM algorithm has strong potential as an effective approach for supporting the automatic identification of oil palm leaf conditions based on digital image processing.