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All Journal Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Journal of Science and Engineering
Mohammad Soeharto
Universitas Nurul Jadid
Computer Science & IT

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Perancangan Sistem Informasi Pembayaran SPP Dan Pembayaran Pendaftaran Peserta Didik Baru Sekolah Dasar Dalam Bentuk Website Menggunakan Framework Laravel Mohammad Soeharto; Abdul Karim; Ahmad Supriadi
Journal of Science and Engineering Vol. 1 No. 1 (2025)
Publisher : CV. Akira Java BUlu

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Proses pembayaran SPP dan pendaftaran peserta didik baru di tingkat sekolah dasar seringkali masih dilakukan secara manual, yang berisiko menimbulkan kesalahan pencatatan, keterlambatan, dan minimnya transparansi. Penelitian ini bertujuan untuk merancang sistem informasi pembayaran berbasis website menggunakan framework Laravel guna meningkatkan pengelolaan administrasi keuangan sekolah. Pengembangan sistem dilakukan dengan metode waterfall yang mencakup tahapan analisis, perancangan, implementasi, dan pengujian. Laravel dipilih karena mendukung arsitektur Model-View-Controller (MVC), yang mempermudah pemeliharaan dan pengembangan sistem. Sistem yang dirancang mencakup fitur manajemen data siswa, pencatatan pembayaran SPP dan pendaftaran, laporan keuangan, serta autentikasi pengguna berdasarkan peran.
Mengklasifikasi Mata Uang Lima Ribu Rupiah dan Dua Ribu Rupiah dengan Menggunakan Algoritma CNN Mohammad Soeharto; Mohammad Jeky Hasan; Ahmad Rega Susanto; Dimas Ahmad Fahrezi
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 2 No. 3 (2024): Juli : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v2i3.1407

Abstract

Currency classification is one of the challenges in the field of digital image processing and computer vision which can be applied in various applications, such as ATM machines, automatic money exchange machines, and mobile banking applications. This research aims to develop a classification model that is able to differentiate between 5000 thousand rupiah and 2000 thousand rupiah currency using the Convolutional Neural Network (CNN) algorithm. CNN was chosen because of its ability to recognize complex visual patterns and specific features from images. The dataset used in this research consists of 10 currency images of 5000 thousand rupiah and 10 images of 2000 thousand rupiah taken in lighting conditions and viewing angles vary and are classified into 2 classes. The data is then processed and normalized to increase model accuracy. The proposed CNN model, namely the Squential Model, consists of several convolution layers, pooling layers, and fully connected layers which are optimized to detect visual differences between the two types of currency.
Co-Authors Abdul Karim Ahmad Rega Susanto Ahmad Supriadi Dimas Ahmad Fahrezi Mohammad Jeky Hasan