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All Journal Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Publikasi Ilmu Komputer dan Multimedia
Ahmad Fariz Fuady
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Computer Science & IT Industrial & Manufacturing Engineering

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Perancangan Aplikasi Pengajuan Magang di Fakultas Syariah dan Hukum UINSU Berbasis Web Ahmad Fariz Fuady; Dwiky Oldi Amsyah; Suhardi Suhardi
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i2.1283

Abstract

The internship application process at the Faculty of Sharia and Law, UIN Sumatera Utara, is still conducted manually, leading to various issues such as delayed information, unstructured administration, and inefficient communication between students, faculty, and partner institutions. To address these problems, a web-based internship application system was developed to help students access internship information, apply online, and assist faculty in verifying and monitoring the process. The development of this application uses the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. During the design phase, Unified Modeling Language (UML) was used, including use case diagrams, activity diagrams, and class diagrams to visualize the system’s structure and workflow. This application offers key features such as a list of partner institutions, internship vacancies, document submission, and application status notifications. The implementation of this web-based system is expected to make the internship application process faster, more transparent, well-organized, and supportive of the academic digitalization efforts at the Faculty of Sharia and Law, UIN Sumatera Utara.
Implementasi Algoritma Convolutional Neural Network (CNN) untuk Pengenalan dan Klasifikasi Buah Berdasarkan Citra Digital Ahmad Fariz Fuady; Dwiky Oldi Amsyah; Muhammad Farhan; Rusma Riansyah; M. Dayyan Dhiyaul Haq
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol. 4 No. 2 (2025): Mei: Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupikom.v4i2.4116

Abstract

Object recognition, particularly fruit classification, plays a crucial role in various fields, ranging from agricultural automation to digital marketplaces. This study proposes a fruit classification system based on RGB images, developed using a Convolutional Neural Network (CNN) architecture consisting of convolutional layers, pooling layers, fully connected layers, and dropout for model stability. The model was trained using the Adam optimization algorithm on an augmented dataset to enhance data variation and reduce overfitting. The resulting model achieved an average accuracy of 98%, demonstrating the reliability of CNNs in pattern recognition tasks. To enhance usability, the model was integrated into a graphical user interface (GUI) built with MATLAB R2023b App Designer, allowing users to add datasets, train the model, and predict new images without writing any code. The findings highlight that while the model performs well, its accuracy remains dependent on consistent image backgrounds; therefore, expanding the variety of fruit types and background conditions in the dataset is essential to improve the system's robustness in real-world applications.
Co-Authors Dwiky Oldi Amsyah M. Dayyan Dhiyaul Haq Muhammad Farhan Rusma Riansyah Suhardi, Suhardi