<![CDATA[Memes generally in the form of images that are the result of the production of people who are used to comment on an event followed by certain templates from popular online images. The spread of memes has become phenomenal and has become very popular in recent years, ranging from lightly charged memes such as memes about jokes, meme quotes to memes with heavy content such as memes about government, education, and the latest news. So that a system is made that can classify types of memes into negative or positive memes by applying the image process and OCR Tesseract combined with the Naive Bayes algorithm. OCR Tesseract is needed to recognize text contained in an image, while the Naive Bayes Algorithm is an algorithm used to find the highest probability value to classify the test data in the most appropriate category. In this study, the test data are meme documents. The results obtained from this application are the level of accuracy of meme classification analyzed is 73.3% from 15 test data and the average classification process time is 1.328 ms. Memes that have been classified for accuracy value depend on the results of OCR using the tesseract engine]]>
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