<![CDATA[Smartphones with powerful camera sensor capabilities can be used to analyze photos and object recognition. Food is one of the popular photography objects and seeing it makes you want to cook or taste it. Cooking requires recipes as a tool to make dishes because not everyone knows how to make dishes. Food recipe search techniques with food image input are needed because not everyone knows the name of the food made. There are several steps in the method carried out to do the introduction of food types namely preprocessing, feature extraction using the Color Moments and Gray Level Counseling Matrix (GLCM) method, feature selection using the Information Gain method and classification using the Weighted K-Nearest Neighbor (NWKNN) method. Tests were carried out to determine the accuracy of the NWKNN method and also to know the effect of the Information Gain feature selection. The results of testing with the K-Fold Cross Validation method obtained the highest average accuracy of 92.37% by dividing the test data by 30, the number of features by 10, the value of k on the NWKNN by 3 and calculating distances using Cosine Similarity. On other hands, the testing of the Information Gain effect resulted in the highest accuracy of 86.96% with the 15 best features. It can be concluded that the NWKNN method can answer the problem of unbalanced data and Information Gain can find out the best features for classification.]]>
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