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All Journal JUSTIFY : Jurnal Sistem Informasi Ibrahimy SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat Khatulistiwa : Journal of Artificial Intelligence
Ahmad Halimi
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Public Health Other

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OPTIMASI KEAMANAN DATA PENERIMAAN MAHASISWA MENGGUNAKAN AES-256, SHA-256, DAN BASE64 Ahmad Halimi; Abu Tholib; Moh. Ainol Yaqin
JUSTIFY : Jurnal Sistem Informasi Ibrahimy Vol. 3 No. 1 (2024): JUSTIFY : Jurnal Sistem Informasi Ibrahimy
Publisher : Fakultas Sains dan Teknologi, Universitas Ibrahimy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/justify.v3i1.5107

Abstract

In this era of extraordinary information technology, data security is a priority, especially the process of admitting new students (PMB) to tertiary institutions. This process involves collecting sensitive personal data from thousands of prospective students each year. To protect this data, this research applies 256-bit Advanced Encryption Standard (AES), Secure Hash Algorithm 256 (SHA-256), and Base64 encryption methods. AES-256-CBC is known to be effective in maintaining data security with a high level of security. SHA-256 enhances security further by generating a unique hash that verifies data integrity. Meanwhile, Base64 converts binary data into a more manageable text format. This research also includes testing encryption and verification speed using the Laravel framework. The application of this method is expected to increase trust and meet strict data security standards in the PMB information system, guarantee comprehensive data protection and improve system integrity.
Application of Siamese Neural Network for Offline Signature Verification Based on Similarity Level Ahmad Halimi
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 1 No 2 (2025): Juni 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

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Abstract

The increasing demand for secure and accurate identity verification systems has led to the development of various biometric technologies, one of which is signature verification. Despite the rise of digital authentication methods, signatures remain a widely accepted and legally binding form of identity verification, especially in paper-based systems. This research explores the application of the Siamese Neural Network (SNN) method for offline signature verification based on image similarity levels. The study aims to reduce human error, speed up verification time, and increase accuracy in identifying genuine and forged signatures. The dataset used in this study consists of 210 signature images collected from 14 respondents, including 7 with genuine signatures and 7 with forged signatures (categorized as random, unskilled, and skilled forgeries). Preprocessing steps such as scanning, resizing, and CSV data generation were conducted to optimize input for the SNN model. The model was trained using contrastive loss to learn signature similarity representations and was evaluated using a confusion matrix. The training dataset included 147 image pairs, and the testing set contained 63 image pairs, resulting in 168 prediction possibilities. The SNN achieved an accuracy rate of 94%, correctly predicting 159 cases while misclassifying 8 due to image quality and unclear signature strokes. These results indicate that the Siamese Neural Network is effective for offline signature verification and demonstrates strong potential for real-world implementation in identity authentication systems. This research contributes to the field of computer vision, particularly in biometrics, by providing an efficient, learning-based approach to signature validation using deep learning techniques.
PELATIHAN PENGGUNAAN SISTEM CAT UNTUK UJIAN KOMPETENSI BAHASA ASING DENGAN LARAVEL DI UNIVERSITAS NURUL JADID Ahmad Halimi; Siti Mariatul Munawaroh; Nanda Restu Pertiwi; Selly Dwi Fitriyanti; Subaidatul Jannah; Andini Pratiwi
SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat Vol 1 No 01 (2024): Oktober
Publisher : CV. Laskar Karya

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Abstract

Lembaga Bahasa Universitas Nurul Jadid menghadapi tantangan dalam pengelolaan ujian kompetensi bahasa asing, terutama dalam administrasi, efisiensi waktu, serta keamanan dan integritas ujian. Sistem manual yang ada kurang efektif, terutama untuk mengelola jumlah peserta yang besar dan menjaga standar keamanan. Digitalisasi dalam pendidikan menuntut integrasi teknologi seperti Computer Assisted Test (CAT), yang terbukti efektif di institusi lain. Program pengabdian ini mengimplementasikan Sistem CAT berbasis Laravel untuk memenuhi kebutuhan lembaga dan mengatasi keterbatasan sistem saat ini. Tahapan implementasi meliputi: Persiapan dengan pembuatan panduan dan modul pelatihan CAT; Pelatihan untuk staf dan mahasiswa; Evaluasi melalui survei untuk mengukur efisiensi, keamanan, dan kepuasan pengguna; serta Monitoring berkelanjutan. Hasil evaluasi menunjukkan peningkatan efisiensi manajemen ujian hingga 50%, keamanan ujian yang lebih baik, dan kepuasan mahasiswa yang meningkat. Framework Laravel dipilih karena fleksibilitasnya dalam memenuhi kebutuhan akademik lembaga. Sistem CAT berbasis Laravel ini berhasil meningkatkan efektivitas dan efisiensi ujian, serta mendukung peningkatan standar akademik. Disarankan untuk melakukan pengembangan berkala agar sistem tetap relevan dengan perkembangan teknologi pendidikan.
Pelatihan Single Sign-On untuk Meningkatkan Efisiensi Pengelolaan Sistem Informasi Kampus Ahmad Halimi; Aisatur Ridho; Amelia Silvia Putri; Vina Yusrolana
SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat Vol 2 No 01 (2025): Oktober
Publisher : CV. Laskar Karya

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Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan memberdayakan pengelola sistem informasi di Universitas Nurul Jadid Paiton Probolinggo melalui pelatihan penggunaan teknologi Single Sign-On (SSO) untuk mengatasi permasalahan kesulitan akses sistem yang menyebabkan pemborosan waktu, keluhan pengguna, dan beban administrasi tinggi. Khalayak sasaran adalah 35 orang pengelola sistem informasi, staf IT, dosen, dan perwakilan unit kerja yang mengalami kesulitan mengelola akun di berbagai sistem aplikasi kampus. Metode pelaksanaan mencakup identifikasi masalah melalui Focus Group Discussion (FGD), workshop pemahaman manfaat dan cara kerja SSO, pelatihan praktis penggunaan sistem SSO, uji coba dengan pengguna terbatas, serta pendampingan selama 3 bulan hingga masyarakat kampus dapat menggunakan sistem dengan lancar. Hasil kegiatan menunjukkan peningkatan pengetahuan peserta tentang manfaat SSO sebesar 86%, waktu yang dibutuhkan pengguna untuk mengakses sistem berkurang dari 6-8 menit menjadi kurang dari 5 detik, tingkat kepuasan pengguna mencapai 87%, dan keluhan terkait lupa password berkurang hingga 60%. Luaran kegiatan berupa sistem SSO yang memudahkan civitas akademika mengakses semua aplikasi kampus dengan satu kali login, modul pelatihan yang dapat digunakan untuk sosialisasi berkelanjutan, dan tim pengelola internal yang mampu melayani kebutuhan pengguna secara mandiri. Kegiatan ini memberikan dampak jangka panjang berupa peningkatan kualitas layanan digital kampus dan kepuasan masyarakat kampus dalam menggunakan sistem informasi.
Classification of Poisonous Ornamental Plants Using CNN and ResNet Methods Ahmad Halimi; Eka Herliana Agustini; Nur Diyana Kholidah
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 1 (2025): Desember 2025
Publisher : Khatulistiwa : Journal of Artificial Intelligence

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Abstract

This research, titled "Classification of Poisonous Ornamental Plants Using CNN and ResNet Methods," defines poisonous ornamental plants as those containing toxic substances that can cause pain, allergies, or even death. These plants come in many different varieties, each with its own unique appeal. Many laypeople still find it difficult to distinguish poisonous from non-poisonous ornamental plants, especially in household environments that pose a risk to children and pets. Therefore, vigilance is needed in recognizing these plants. This study aims to develop a classification system for poisonous and non-poisonous ornamental plants using CNN methods with and without ResNet architecture. Furthermore, this study also aims to implement the trained model into a web application using Flask, so users can easily upload images of ornamental plants and obtain information about their potential toxicity in real-time. This research method uses deep learning techniques, specifically Convolutional Neural Network (CNN) with ResNet-50 and regular Convolutional Neural Network (CNN) or without ResNet with data divided into (70% training, 15% validation, and 15% testing). The test results for ResNet showed an accuracy of 98.25%, while the test results for regular CNN reached an accuracy of 87.47%. These accuracy results indicate that CNN with ResNet is superior for classifying poisonous and non-poisonous ornamental plants compared to CNN without ResNet
Co-Authors Abu Tholib Aisatur Ridho Amelia Silvia Putri Andini Pratiwi Eka Herliana Agustini Nanda Restu Pertiwi Nur Diyana Kholidah Selly Dwi Fitriyanti Siti Mariatul Munawaroh Subaidatul Jannah Vina Yusrolana Yaqin, Moh. Ainol