Komparasi CNN dan SVM untuk Klasifikasi Penyakit Daun Jagung Berbasis CLAHE dan Explainable AI

Abstract

Corn productivity losses from leaf diseases remain a serious problem, while manual observation is subjective and accurate models are hard to trust when their decisions cannot be traced. The contribution of this research lies in controlled testing that isolates the contribution of CLAHE from architectural influences, establishing preprocessing parameters based on empirical testing, and directly comparing both methods under identical data and treatment conditions. This study compares Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for corn leaf disease classification under equivalent conditions, using CLAHE preprocessing and LIME. Testing used 4,188 images in four classes. The CNN based on EfficientNet-B0 reached 94.98%, outperforming the SVM based on GLCM and color histogram features at 92.46%. This 2.52-point margin is narrower than assumed, and the additional SVM errors concentrate in one class. A hybrid combining EfficientNet-B0 features with an SVM classifier reached 95.33%, indicating the CNN advantage stems from its feature representation rather than its decision layer. The SVM also predicted one hundred sixty times faster without a graphics accelerator. An ablation test shows the CLAHE benefit is method-dependent: the SVM gained 1.19 points, the CNN did not. LIME shows most influential regions fall on lesion areas, though some appear in the background. These findings keep the SVM relevant when computational resources are limited.