<![CDATA[Technology makes it easier for humans. One of them is the ease of finding food on the internet. However, in general, search engines are available using text queries with image file names, a textual based approach. It is difficult to be done on large-scale imagery. Image search based on visual image content commonly known as image retrieval system based on content or Content Based Image Retrieval (CBIR) can be used as a solution. Food image has different colors and textures. The texture feature extraction method used in this research is Local Binary Pattern (LBP) and for the color feature extraction is Color Histogram. The image used is 444 data, 413 data as data training and 31 data as data testing. Based on feature extraction, similarity can be calculate using Euclidean distance. The result get by calculating Mean Average Precision (MAP). The best MAP obtained when the n value is 2 with MAP 0,919354 which n is the number of document that displayed on result. For the feature comparison testing, the use of color features only provides better results than using the texture feature or both features]]>
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