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All Journal Jurnal Informatika dan Kesehatan INOVTEK Polbeng - Seri Informatika
Kustiyono
Universitas Ngudi Waluyo
Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Medicine & Pharmacology Public Health

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Analisis Kesamaan Gambar Menggunakan Metode Ekstraksi Fitur Agung Wibowo; Ade Pratama; Kustiyono
Jurnal Informatika dan Kesehatan Vol. 1 No. 1 (2024): IKN : Jurnal Informatika dan Kesehatan
Publisher : Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35473/ikn.v1i1.3040

Abstract

This research begins with a discussion of imaging systems. Many user interactive systems are built with basic concepts. However, they fail to meet user needs or attract user attention. As a result, in the last few years, research has concentrated on new and up-to-date specifications that keep users attracted to the expected way to interact well. The system focuses on using a variety of mathematical methods to take images from a large collection of images based on color projection. Images are grouped into subgroups with threshold values before using the suggested technique. The combination of colors R, G, B is considered for taking images in this research through the observed results so that we can produce more effective results compared to previously existing ones.   Abstrak Penelitian ini memulai dengan membahas sistem pengambilan gambar. Banyak sistem interaktif pengguna dibangun dengan konsep dasar. Namun, mereka gagal memenuhi kebutuhan pengguna atau menarik perhatian pengguna. Akibatnya, dalam beberapa tahun terakhir, penelitian telah berkonsentrasi pada spesifikasi baru dan terkini yang membuat pengguna tertarik dengan cara yang diharapkan untuk berinteraksi dengan baik. Sistem ini berfokus pada penggunaan berbagai metode matematika untuk mengambil gambar dari koleksi gambar besar yang didasarkan pada proyeksi warna. Gambar dikelompokkan menjadi subkelompokan dengan nilai ambang batas sebelum menggunakan teknik yang disarankan. kombinasi warna R, G, B dipertimbangkan untuk pengambilan gambar dalam riset ini melalui hasil yang diamati agar kita dapat menghasilkan hasil yang lebih efektif dibandingkan dengan hasil yang sudah ada sebelumnya.
Identification of Digital Signature Patterns Based on The CNN Method at Almas’udiyyah Islamic Boarding School Muhammad Ulumul Ikhsanil Huda; Kustiyono
INOVTEK Polbeng - Seri Informatika Vol. 9 No. 2 (2024): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/74bq6m83

Abstract

This study aims to identify digital signature patterns using the Convolutional Neural Network (CNN) method at Al Mas’udiyyah Islamic Boarding School. Digital signatures are an essential form of authentication in electronic transactions. Using MATLAB, we developed a CNN model to classify signatures and evaluate its accuracy. The dataset comprises images of students' signatures. The research stages included collecting 60 signature images for training data and 30 signature images for testing data, which were then acquired using a scanner. The results show that the Convolutional Neural Network method can recognize each signature image with high accuracy during the testing process. Al Mas’udiyyah Islamic Boarding School frequently requires verification processes for administrative purposes, such as signing attendance sheets and documents. With a CNN-based digital signature verification system, the boarding school can ensure the security and authenticity of signatures automatically, reducing the risk of forged signatures and increasing efficiency. The CNN model developed in this study achieved an accuracy of 86% in identifying genuine and forged signatures.
Co-Authors Ade Pratama Agung Wibowo Muhammad Ulumul Ikhsanil Huda