PEMANFAATAN METODE K-NEAREST NEIGHBOR PADA KLASIFIKASI IMAGE BERDASARKAN POLA FITUR DAN TEKSTUR - Nurul Fuad (Joutica, 2017)
Content-based image search can use Content Based Image Retrieval (CBIR). CBIR works by measuring the similarity of query images with all the images in the database so that the query cost is directly proportional to the number of images in the database. Limiting the range of image search by way of classification is one way to reduce the query cost on CBIR. Application of K-Nearest Neighbor method aims to classify the image as well as to measure the level of accuracy and time of classification. In this study built a software that can extract the color and texture features of an image by using the Color Histogram method and the Edge Histogram Descriptor. The results of the feature extraction process are then used by the software in the learning process and classification by the K-Nearest Neighbor method. The software is built with structured analysis and design methods then implemented using VB.net programming language The final result of classification is then tested with parameter level accuracy and classification time. The test results show that the combination of color and texture features provides a higher level of accuracy than classification based on features and textures but requires longer classification time.