Handwriting is still widely used as a research topic in computer vision topics. Two important parameters to consider are classification performance and classification time. Local Binary Pattern (LBP) is a method of extracting shape features in handwriting, but the number of features generated is quite large, resulting in increased classification time. This research proposes a method to speed up the classification time by dividing the LBP features into several grids. It is found that the proposed method can provide good performance at 90.99% accuracy and sufficient speed, on LBP parameter [33], 5x5 grid and KNN parameter k=3.