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Journal : informal informatics journal

Segmentasi Citra Tanda Tangan Menggunakan Fitur Titik SURF (Speeded Up Robust Features) dan Klasifikasi Jaringan Syaraf Tiruan hidayat, muhamad arief; retnani, windy eka yulia; Firmansyah, Diksy Media; Santika, Gayatri Dwi; Furqon, Muhammad ‘Ariful
INFORMAL: Informatics Journal Vol 9 No 3 (2024): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i3.53514

Abstract

Signature image classification is an important field of image processing. One of the stages of signature classification is segmentation. The segmentation process aims to detect image pixels that are part of the signature and separate them from text or logo pixels in a document image. There is a signature segmentation technique using interest points extracted using the SURF (Speeded Up Robust Features) algorithm [1] In this technique, a connected component pixel will be considered part of the signature if it has more SURF points in common with the database connected component pixel signature. Compared to the similarity with the database connected component non-signature pixels. This method is able to provide good accuracy results for signature pixel segmentation. However, the recall value is relatively low, namely 56%. This is because many connected component logos are considered as connected component signatures. In this study, signature segmentation was carried out using SURF points by adding two things: 1) using internal connected component characteristics as additional classification atributs: extent, solidity, ratio, and circularity 2) using an Artificial Neural Network classification algorithm to assist the segmentation process. The test results show that the proposed method improves segmentation quality by an average of 20.7% for an increase in accuracy, an average of 22.4% for an increase in precision, and an average of 18.6% for an increase in recall. When compared with the results reported in (Ahmed et al., 2012), the recall has increased by 38.3% - 42.8%
Co-Authors Afandy Maulana Pangestu Ahmad Sonny Pranata Albert Dewanata Mahrahillah Alifian Sukma, Alifian Annisa Fitri Maghiroh Harvyanti Apol Pribadi Ar Ruhimat, Qurrota A'yuni Ari Cahaya Puspitaningrum Ati Kusmiati Balthasar Watunglawar Bayu Taruna Widjaja Putra Benny Ridwan Susanto4 Deana Nathania Damayanti Deny Hermansyah Dwi Wijonarko Eki Saputra Eki Saputra El Maidah, Nova Endah Septa Sintiya Fadil Abdillah Suyudi Fajariyanto, Gama Wisnu Fazrol Rozi, Fazrol Firmansyah, Diksy Media Gama Wisnu Fajarianto Gama Wisnu Fajarianto Gayatri Dwi Santika Gun Faisal Harry Soepandi Herman Cahyo Diartho Hidayat, Muhamad Arief Iqbal Al Mahdi Irawan, Hendry Sakti Kartika, Vera Katarina Leba Komaria, Vida Leba, Katarina Madani, Anis Mochamad Bagoes Alfarazi Mohammad Ovi Sanjaya Mohammad Zarkasi Muhamad Arief Hidayat Muhammad Fikri Haikal Muhammad Zuama Al Amin Najwa, Nina Fadilah Naura, Lintang Arsa Nico Wibiyanto Nina Najwa Nova El Maidah Nur Aini Rakhmawati Okfalisa Oktavia, Nelly Priza Pandunata Puspaningrum, Diah Putri, Ikfira Agustina Qurrota A'yuni Ar Ruhimat Qurrota A’yuni Ar Ruhimat Rakhmawati, Nur A retnani, windy eka yulia Rita Sari Rizky Alfanio Atmoko Rizqi Alvian, Muhammad Bagus Roslan Hasni S Slamin Saiful Bukhori Saudi Efendi Sayekti Harits Suryawan Setiawan, Juni Suyudi, Fadil Abdillah Vega Kartika Sari Vera Kartika Windi Eka Yulia Retnani Yanuandika Akbar Yanuar Nurdiansyah, Yanuar Yanuarti, Rizky Yosari Dwi Fadhillah Yudha Alif Auliya Yudha Alif Auliya Zarkasi, Mohamad Zarkasi, Mohammad