PENGENALAN TULISAN TANGAN KARAKTER HIRAGANA MENGGUNAKAN DCT, DWT, DAN K-NEAREST NEIGHBOR

Research to recognize hiragana character based image processing has been widely practiced and even the accuracy level is close to 100%. However, the input image that used is still in the form of japanese characters print-out while the handwriting has not been studied. So in this study tested the recognition of hiragana letters derived from handwriting format. Jpeg. Of the several related studies, the most commonly used compression approach for JPEG images is the DCT and DWT algorithms, so both algorithms are used in this study to be tested and compared their performance. In the system tested 45 images of 3 people handwriting hiragana character with KNN-based classification where previously 45 different images of the 3 people are trained by each DWT and DCT algorithms. The result is based on the distance parameters that exist in the KNN algorithm, the DWT algorithm is superior to the DCT algorithm. The achievement of the maximum accuracy level obtained for each DWT-DCT algorithm is on the cityblock distance parameter 82.61% (DWT) and correlation distance 58.70% (DCT).