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dc.contributor.authorKumalasanti, R. Arum
dc.contributor.authorErnawati
dc.contributor.authorDwiandiyanta, B. Yudi
dc.date.accessioned2016-02-06T04:48:38Z
dc.date.available2016-02-06T04:48:38Z
dc.date.issued2015-10-15
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Yadav, M., Kumar, A., Patnaik, T. & Kumar, B., (2013) “A Survey on Offline SignatureVerification” International Journal of Engineering and Inovative Technology, Vol. 2(7), pp.337-40in_ID
dc.identifier.issn1412-9612
dc.identifier.urihttp://hdl.handle.net/11617/6588
dc.description.abstractTanda tangan merupakan atribut biometrik yang penting dari individu yang dapat digunakan sebagai identitas. Penggunaan tanda tangan merupakan cara yang alami dan tradisional sebagai identitas yang sah. Hal ini membuat keberadaan tanda tangan menjadi penting, sehingga diperlukan adanya sistem yang digunakan untuk memberi pengamanansupaya tidak disalahgunakan oleh pihak yang tidak bertanggungjawab. Berbagai pendekatan telah diusulkan dalam pengembangan identifikasi tanda tangan yang bertujuan untuk mengidentifikasi tanda tangan sesuai kepemilikannya. Penelitian ini akan membahas identifikasi tanda tangan statik yang terdiri atas dua proses utama yaitu pelatihan dan pengujian. Ukuran citra yang digunakan adalah 256x256 piksel. Pada tahap pelatihan, citra tanda tangan dikenai beberapa proses yaitu threshold, alihragam wavelet Haar, normalisasi, dan kemudian dilatih dengan menggunakan algoritma Jaringan Syaraf Tiruan (JST) Backpropagation. Tahap pengujian memiliki proses yang sama seperti pada tahap pelatihan namun di akhir proses akan dilakukan perbandingan antara data citra yang telah tersimpan dengan citra pembanding. JST dapat bekerja secara optimal apabila dilatih dengan menggunakan data input yang sudah dipertimbangkan ukuran, parameter, dan jumlah node pada jaringan. Hasil optimal didapat dengan menggunakan JST yang memiliki dua hidden layer, masing-masing 20 dan 10 node, alihragam waveler Haar pada level 4, learning rate 0,12. Pelatihan dan pengujian pada tahap identifikasi ini, masing-masing memberikan akurasi sebesar 95,56% dan 100%.in_ID
dc.language.isoidin_ID
dc.publisherUniversitas Muhammadiyah Surakartain_ID
dc.subjectbackpropagation identifikasiin_ID
dc.subjectJSTin_ID
dc.subjecttanda tanganin_ID
dc.subjectWavelet Haarin_ID
dc.titleIDENTIFIKASI TANDA TANGAN STATIK MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION DAN WAVELET HAARin_ID
dc.typeArticlein_ID


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