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All Journal DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Cosmic Jurnal Teknik
Simbolon, Zianah Nafisah
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perancangan dan Implementasi Sistem Informasi Ekstrakurikuler Sekolah di SMP Harapan 3 Medan Menggunakan Model Incremental Risky, Tengku Tanzil Azhari; Putra, Fahrialdy Febriansyah; Zebua, Jelita Rahmah; Batubara, Qisti Azraladiba; Sitorus, Puan Syaharani; Simbolon, Zianah Nafisah; Edilia, Fazila Nazifa; Rinaldi, Rio; Sitorus, Dhafa Hibrizi; Zufria, Ilka
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.7121

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi ekstrakurikuler berbasis web di SMP Harapan 3 Medan. Sistem ini dikembangkan untuk mengatasi kendala pengelolaan ekstrakurikuler secara manual yang sering menyebabkan data hilang, jadwal tumpang tindih, dan informasi tidak tersampaikan dengan baik. Metode incremental digunakan agar sistem dapat dibangun secara bertahap sesuai kebutuhan pengguna. Fitur utama meliputi pendaftaran online, manajemen data, serta pemberitahuan ekstrakurikuler. Hasil implementasi menunjukkan peningkatan efisiensi pengelolaan dan akses informasi real-time bagi siswa dan pembina secara terstruktur dan terintegrasi.
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 Batubara, Qisti Azraladiba Edilia, Fazila Nazifa Harahap, Lailan Sofinah Ilka Zufria Putra, Fahrialdy Febriansyah Rio Rinaldi Risky, Tengku Tanzil Azhari Sitorus, Dhafa Hibrizi Sitorus, Puan Syaharani Utami, Andita Zebua, Jelita Rahmah