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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal CoreIT JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Penelitian Tarbawi: Pendidikan Islam dan Isu-Isu Sosial Jurnal Teknologi Dan Sistem Informasi Bisnis Syntax: Journal of Software Engineering, Computer Science and Information Technology Instal : Jurnal Komputer Melek IT: Information Technology Journal Ilmu Komputer untuk Masyarakat Data Sciences Indonesia (DSI) Internet of Things and Artificial Intelligence Journal Journal of Artificial Intelligence and Digital Business Jurnal Ilmiah Sistem Informasi Indonesian Journal of Education and Development Research JRIIN :Jurnal Riset Informatika dan Inovasi Cosmic Jurnal Teknik Journal of Educational Science and E-Learning
Harahap, Lailan Sofinah
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Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Analisis Algoritma Convolutional Neural Network (CNN) untuk Pengenalan Pola Tangan Berdasarkan Citra Garis Telapak Tangan Edilia, Fazila Nazifa; Harahap, Lailan Sofinah; Utami, Andita; Simbolon, Zianah Nafisah
Cosmic Jurnal Teknik Vol 3 No 1 (2026): Februari
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosmic.v3i1.1495

Abstract

Individual identification is an important aspect of security and biometric authentication systems. One biometric characteristic with high potential is palm print patterns, as each individual has a unique pattern that is difficult to forge. Therefore, it is necessary to develop an individual identification system based on palm print images using a pattern approach with the Convolutional Neural Network (CNN) method. Test results show that this method is capable of identifying individuals with a fairly high level of accuracy, depending on the image quality and training parameters as well as the network architecture used.
Co-Authors Abdillah, Rizky Alamsyah, Muhammad Fadlan Ananda, Surizky Aptanta, Dimas Aqila Armansyah Arrafi, M. Rifqi Aryanda, Hafiz Aznawi, Nasrul Mahruf Edilia, Fazila Nazifa Fadillah, hasti Firjatullah, Maulana Furqon, Mhd Gimnastiar, Alfin Hsb, Adinda Tarisyah Irsyad, Raihan Maulana Januar, Bagus Kembaren, Muhammad Fajar Hidayah Maria Ulfa Muhammad Faruqi Muhammad Ikhsan Nasution, Najwa Fadiyah Pangestu, Aji Dewo Panjaitan, Rosita Prayoga, M Irsan Putri, Laila Ali Ramadhan, M. Wahyu Rifaldy, Faqih Salsabila, Awalia Saragih, Fauhan Alfarizi Silalahi, Yayi Naulia Simbolon, Zianah Nafisah Sriani Suci Wulandari Utami, Andita Widiya, Widiya