Classification of Vegetable Types Using Singular Value Decomposition (SVD) and K-Nearest Neighbor (KNN) Algorithms - Jong, Fenny; Herwindiati, Dyah Erny (Universitas Pahlawan Tuanku Tambusai, 2024)
Vegetables are widely grown in Indonesia, but sometimes they can be prepared poorly and pose risks to consumers. To solve this problem, we need a high-quality system that can identify good and safe vegetables. This study aims to create a vegetable classification system using pictures and computer algorithms. The system analyzes different types of vegetable images, including color and shape. It uses special techniques called Singular Value Decomposition (SVD) and K- Nearest Neighbor (KNN) to classify the vegetables based on their features. The researchers used a dataset of 121 vegetable images, which were divided into 73 training images and 48 test images. The results showed that the system was able to classify the vegetables with a high accuracy rate of 85.42%. This study has the potential to help improve the quality of vegetables and contribute to the development of automated systems in the agricultural industry.