Machine learning vegetable pesticide identification based on color image using k-nearest neighbor method (KNN).

The color characteristics of pesticide vegetables can be seen with the naked eye, but there are inaccuracies in the assessment that continue to be carried out so that time efficiency is not met. KNN processes data from the color of vegetables with pesticides by classifying all images of vegetables. The results of the analysis showed that in this test the authors found that cauliflower vegetables did not contain pesticides, where the results obtained from color extraction were carried out from training results, and the test stated that cauliflower vegetables were vegetables that did not contain pesticides or were suitable for consumption. The results of this test use 30 test data with attributes and 3 species in the data classification. The test results show the K-Nearest Neighbor method in data classification has a good percentage of accuracy when using random data. The percentage of variation in the value of K K-Nearest Neighbor 3,5,7,8,9 as a percentage of 100%. [ABSTRACT FROM AUTHOR]