Riduwan Purnaminyan, . (2021) IDENTIFIKASI CITRA TANDA TANGAN ASLI DAN PALSU MENGGUNAKAN GRID ENTROPY, PRINCIPAL COMPONENT ANALYSIS, DAN SUPPORT VECTOR MACHINE. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
Signature is a person's writing that has a certain writing style, is symbolic, and tends to be different for everyone. Signatures can be used on a document as evidence that someone agrees to the contents of the document and is bound by the rules and responsibilities therein. By knowing how much influence the signature has, it often makes someone want to imitate or fake a signature for personal gain. Therefore, it is necessary to have a system that can help solve this problem in identifying the authenticity of the signature. In this study, grid entropy feature extraction method is used to extract signature image characteristic features, principal component analysis dimension reduction is used to reduce computational load without significantly reducing accuracy, and lastly support vector machine classification algorithm is used to produce optimal classification results on small scale datasets. The results obtained are 95% accuracy, 96.7% precision, and 94.7% f1-score so that the model can be said to be successful in identifying the original signature image and the fake signature image well.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No. Panggil : 1710511052] [Pembimbing 1 : Didit Widiyanto] [Pembimbing 2 : Mayanda Mega Santoni] [Penguji 1 : Iin Ernawati] [Penguji 2 : Nurul Chamidah] |
| Uncontrolled Keywords: | Grid Entropy, Principal Component Analysis, Support Vector Machine, Tanda Tangan |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Riduwan Purnaminyan |
| Date Deposited: | 21 Dec 2021 07:36 |
| Last Modified: | 21 Dec 2021 07:36 |
| URI: | http://repository.upnvj.ac.id/id/eprint/11175 |
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