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All Journal Paradigma Techno Nusa Mandiri : Journal of Computing and Information Technology Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Infortech Jurnal Teknimedia: Teknologi Informasi dan Multimedia Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Abdimas Prakasa Dakara
Faruq Aziz
Universitas Nusa Mandiri
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Perancangan Sistem Penyewaan Alat Event Berbasis Website Menggunakan Midtrans sebagai Integrasi Payment Gateway pada PT. Bangbewe Production Rian Sutisna; Faruq Aziz
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 1 (2025): Volume 9 Nomor 1 Januari 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i1.14498

Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan sistem penyewaan alat event berbasis website untuk PT. Bangbewe Production dengan metode Waterfall. Sistem ini dirancang untuk mengatasi permasalahan operasional, seperti pengelolaan manual yang tidak efisien dan keterbatasan proses pembayaran. Integrasi payment gateway Midtrans memungkinkan pelanggan menyelesaikan pembayaran secara online dengan metode yang fleksibel dan aman. Sistem ini melibatkan empat aktor utama, yaitu pelanggan, staff operasional, keuangan, dan direktur, dengan masing-masing peran untuk mendukung pengelolaan data, transaksi, dan laporan. Hasil pengujian menggunakan metode black-box menunjukkan bahwa semua fitur berfungsi sesuai spesifikasi, mulai dari registrasi akun hingga laporan keuangan. Evaluasi performa menggunakan GTmetrix menunjukkan waktu pemuatan halaman yang cepat dan interaksi tanpa hambatan, sementara pemeriksaan keamanan melalui Sucuri menunjukkan tingkat keamanan berada pada kategori medium. Dengan fitur yang telah diimplementasikan, sistem ini berhasil meningkatkan efisiensi pengelolaan penyewaan alat, mempermudah transaksi pelanggan, serta mendukung analisis bisnis. Sebagai rekomendasi, peningkatan keamanan sistem dan pengembangan aplikasi berbasis mobile diusulkan untuk memperluas jangkauan layanan dan memberikan pengalaman pengguna yang lebih baik. Sistem ini diharapkan mampu menjadi solusi yang efektif bagi perusahaan dalam menghadapi tantangan bisnis modern di industri penyewaan alat event.
EVALUATING OF DEEP LEARNING MODELS FOR EARLY DETECTION IN MEAT CLASSIFICATION: A STUDY ON BEEF AND PORK DETECTION Taopik Hidayat; Faruq Aziz; Daniati Uki Eka Saputri; Nurul Khasanah
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.370

Abstract

Accurate classification of beef and pork images is crucial for developing reliable automated food inspection systems, particularly due to their visual similarity in color, texture, and muscle fiber patterns. This study aims to comparatively evaluate the performance of multiple deep learning models for binary meat image classification using RGB digital images. Four Convolutional Neural Network (CNN) architectures, namelyInceptionV3, VGG16, ResNet50, and Xception were assessed under identical preprocessing pipelines and hyperparameter settings to ensure a fair comparison. The dataset underwent cropping, resizing to 224×224 pixels, normalization, and augmentation to enhance variability and improve generalization performance. Model effectiveness was measured using accuracy, precision, recall, and F1-score on unseen test data. Experimental results show that InceptionV3 achieved the most balanced classification performance, with a test accuracy of 72% and an F1-score of 0.7. Although Xception obtained higher training accuracy, it exhibited overfitting during testing, while VGG16 and ResNet50 demonstrated comparatively lower classification capability. These findings indicate that InceptionV3 provides a more stable and generalizable architecture for beef and pork image classification. The study highlights the importance of cross-architecture evaluation in developing robust CNN-based systems for automated meat classification.
Co-Authors ., Widiyawati Achmad Rifai Agus Subekti Ami Rahmawati Anton Daniati Uki Eka Saputri Dwi Andriyanto Dwiza Riana Eka Herdit Juningsih Fadillah Said Frisma Handayanna Hermanto Hermanto Hermanto Hermanto Hilman F. Pardede Irmawati Irmawati Irmawati Irmawati irmawati irmawati irmawati Khabib Astoni Kudiantoro Widianto, Kudiantoro Linda Marlinda Nurul Khasanah Rian Sutisna Ruhul Amin Susafa’ati Susafa’ati Syaifur Rahmatullah Taopik Hidayat Taransa Agasya Tutupoly Wahyu Indrarti Windu Gata Windugata Windugata Zico Pratama Putra