Klasifikasi Kualitas Sayuran Menggunakan Metode Support Vector Machine - Ignatius Charles Hans Burwos; I Gusti Ngurah Anom Cahyadi Putra (Jurnal Nasional Teknologi Informasi dan Aplikasinya, 2025)
The advancement of technology, particularly in the field of machine learning, has provided promising opportunities for enhancing agricultural practices. This study presents the development of a Support Vector Machine (SVM) based classification system for assessing the quality of vegetables. The system utilizes image processing techniques, including Histogram of Oriented Gradients (HOG) and color histogram, to extract relevant features from vegetable images. The extracted features are then used to train an SVM model capable of distinguishing between good and bad quality vegetables. The effectiveness of the proposed system was evaluated using a dataset comprising various types of vegetables. The results demonstrate high accuracy and efficiency in classifying vegetable quality, highlighting the potential of machine learning technologies in agricultural management.