<![CDATA[Under conditions that determine whether an apple is good or not, the human eye tends to have a subjective perception due to the color composition factor. Errors often occur because it is done manually. Therefore we need a tool with a system that can choose apples automatically based on their type. So we need a system that can identify the ripeness of apples by implementing SSD-Mobilenet. The purpose of this research is to identify the types of apples using SSD-MobileNet. From the results of the analysis and testing it can be concluded that the test results on data testing with lots of data, namely 50 datasets taken randomly produce an accuracy of 82% and an error of 18%. The number of errors indicates that the classification results are not completely accurate. This can happen due to the lack of training data so that only a few dominant terms are used, causing errors in course costs. However, the results of this accuracy can be used as a reference that assistance using SSD-Mobilenet provides a high value of 82%. So this method can be used to analyze the ripeness of apples.]]>
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