<![CDATA[Palm sugar is one type of sugar that is often used by the community as a sweet taste for cooking, making food and drinks. Palm sugar is made from palm sap or juice from coconut trees, by boiling. To distinguish real and mixed palm sugar, by naked eye it is difficult to tell the difference. Moreover, many people do not understand or lack knowledge about the authenticity of palm sugar circulating in the market. So far, people who buy palm sugar only see the authenticity of palm sugar from its sweet taste or color. For this reason, it is necessary to identify using a digital image of the palm sugar, to determine the type of original palm sugar and mixed palm sugar. This is done so that the public gets information and knowledge so that they can be more observant and thorough in choosing and distinguishing palm sugar on the market by knowing the image characteristics of real palm sugar and mixed palm sugar. The Learning Vector Quantization (LVQ) method is a type of competitive-based network where from the output value given by the neurons in the output layer, only the winning neurons are considered. The winning neuron will undergo weight renewal. From the results of the analysis of calculations carried out with test data, the smallest distance data is obtained, namely at weight 1, so that the test image input on the palm sugar image is included in class 1 or original palm sugar. Thus, the palm sugar test image data is in accordance with the expected result data. Keywords : Palm Sugar, Digital Image Processing, Learning Vector Quantization]]>
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