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All Journal JURNAL MAHKAMAH Jurnal Ilmiah Universitas Batanghari Jambi Pendas : Jurnah Ilmiah Pendidikan Dasar Societas Dei: Jurnal Agama dan Masyarakat Management Studies and Entrepreneurship Journal (MSEJ) Jurnal Ilmiah Manajemen dan Bisnis (JIMBI) Bagimu Negeri : Jurnal Pengabdian Kepada Masyarakat Fibonacci : Jurnal Ilmu Ekonomi, Manajemen dan Keuangan Hikmat : Jurnal Agama Kristen
Kurniawan, Dodi
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Pendas : Jurnah Ilmiah Pendidikan Dasar

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PENGENALAN TULISAN TANGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN DENGAN ALGORITMA PERCEPTRON Kurniawan, Dodi; Sunandar, Hery; Hutabarat, Sumiaty Adelina
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, No. 04 Desember 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v9i4.17918

Abstract

Handwriting is a product of an individual's writing activity. The unique characteristics of handwriting vary from person to person, reflecting their individual traits. However, handwriting is often misused by irresponsible parties. Currently, there is a lack of tools to facilitate the recognition or verification of handwriting. Generally, handwriting recognition can be addressed using Artificial Neural Networks (ANNs). ANNs are artificial representations of the human brain that attempt to simulate the brain's learning processes. In this study, the Perceptron algorithm, a supervised learning method within neural networks, is employed. The research uses 5x5 pixel images of handwriting samples, with the Perceptron algorithm functioning based on the weight values obtained. Testing with the Perceptron algorithm showed that it can recognize three different handwriting samples. The testing concluded at Epoch 3, where the value of Y_in = 39 exceeded the threshold value of 30.
Co-Authors Aji Titin Roswitha Nursanthy apriyanti, dini Fikri , Adi Wibowo Noor Hery Sunandar Hestiani, Irma Hindarsah, Yuliana Hutabarat, Sumiaty Adelina indra Wahyu putra, Christophorus Irfiani, Irfiani Julianda, Rizky Khalida, Laras Ratu Lubis, Hasrita Nur Aisyah NUR KHOLIFAH Putra Danaya, Bayu Sabungan Lubis, Mulia Hoge Sahlan, Fahmi Suherman, Enjang Supratman, Ali Wayhu, Ilham Dwi Widiniarsih, Dewi Mariam Zulfina Adriani