Analisa Hasil Perbandingan Poly Kernel Dan Normalisasi Poly Kernel Pada Support Vector Machine Sebagai Metode Klasifikasi Citra Tanda Tangan

Abstract

Signature is one of the important characteristics that can be used in verification of several documents, one of which is an academic document. Signature verification in the academic environment is important, especially in ensuring the authenticity of the lecturers or teaching staff signatures. Not a few students who choose to falsify the signature of a lecturer or teaching staff in order to facilitate their academic process, this is an important issue especially if the student is actually not eligible and does not meet the criteria to get a signature or endorsement from the lecturer concerned. A technique or method is needed that can help the process of verifying the signatures of lecturers and teaching staff in an academic environment. One technique that might be used is to use image processing techniques. In this study a classification will be made between the genuine and forgery signature images as a verification process of the authenticity of the lecturer signatures obtained by students. The data used is the signature image of a lecturer at Amikom Purwokerto University who was a examiner at the Practical Task Seminar. The method proposed in the classification process uses the Support Vector Machine (SVM) algorithm with two different kernels. Both kernels consist of poly kernel and normalized poly kernel, the selection of the two kernels is used to compare which results are more optimal. The results of this study are SVM by using poly kernel normalization to be able to give better results when compared to using poly kernel only. The results obtained using poly kernel normalization are an accuracy level of 79.43% and a specificity level of 100%.