<![CDATA[Various objects in the form of digital images can be extracted the features. One of the features that can be used in feature extraction is statistical texture features. In this case, the feature extraction is used to identify the characteristics of each type of facial skin because many cases of mistakenly recognize the type of facial skin that has resulted in occurrence of diseases and unwanted things on the face. In this study, the author uses facial skin especially the cheek part as the object of research because the cheek is one part of the T-zone. Local Binary Pattern (LBP) is one of the feature extraction method that uses adjacency/neighboring distance and the number of neighbors that can be used and utilized in the identification process, which can be combined with statistical texture features. The benefit of this study itself is to assist in the initial diagnosis in determining the type of facial skin that is owned. This study uses data as many as 112 female face images obtained by taking data directly in the field (primary data). This study got the highest accuracy result that was equal to 84.62% with adjacency/neighboring distance (R) = 1 and a combination of 3 statistical texture features, that is a combination of mean, skewness and energy.]]>
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