<![CDATA[In today's era, which is supported by technological advances, personal assistance through the face can be carried out by sophisticated machines and robots. One of its applications is personal identification using data mining. But before conducting data training and data classification, it is necessary to carry out the process of data collection and data cleaning or data pre-processing. Currently the face dataset for personal identification at the Caltex Riau Polytechnic in particular is still in the form of raw data, namely in the form of a collection of images that have not been pre-processed. Therefore, this research will perform image preprocessing to clean up the image data that has been collected so that the data can become a cleaner source of information and can be used at a later stage. The data used in this study are image data or photos of Caltex Riau Polytechnic students. At the facial image pre-processing stage using the OpenCV library using the Python programming language. Images collected by 500 students for 5 students. The results of this study are the personal identification dataset of Caltex Riau Polytechnic students consisting of 280 images that have successfully passed the stages of grayscaling, cropping, resizing and Normalization. This dataset is stored in the file data_norm.npz. White box testing is carried out to determine the accuracy of the application of the image pre-processing stage with the test results stating that all functional basis paths applied are in accordance with the cyclometic complexity and its independent path.]]>
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