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All Journal Jurnal Rekayasa Sistem Informasi dan Teknologi PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Aulia, Rifsya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Rancang Bangun Sistem Informasi Pengaduan Mahasiswa Berbasis Web Menggunakan Metode DevOps Aulia, Rifsya; Bintang Aditya Nugroho; Nur Alawiyah Hasibuan
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 2 No. 4 (2025): Mei
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jrsit.v2i4.2417

Abstract

Pengaduan merupakan sarana penting bagi mahasiswa untuk menyampaikan keluhan, aspirasi, dan saran terkait layanan kampus. Saat ini, sistem kotak saran manual di Program Studi Sistem Informasi UIN Suska Riau belum berjalan optimal dan kurang efektif dalam menampung pengaduan mahasiswa. Tujuan dari penelitian ini adalah untuk merancang sistem informasi pengaduan mahasiswa yang berbasis website guna meningkatkan efisiensi, keamanan, dan kecepatan layanan pengaduan. Metode DevOps diterapkan dalam pengembangan sistem untuk mempercepat proses pengembangan dan implementasi, sementara metode PIECES digunakan dalam analisis kebutuhan serta pengumpulan data melalui studi literatur, observasi, dan kuesioner. Temuan dalam penelitian ini mengindikasikan bahwa sistem yang dibangun mampu mempercepat proses penyampaian pengaduan, menjaga kerahasiaan data, serta meningkatkan pelayanan pengaduan secara lebih responsif. Dukungan positif dari mahasiswa memperkuat urgensi pengembangan sistem ini di lingkungan Program Studi Sistem Informasi UIN Suska Riau.
Classification of Breast Cancer Ultrasound Images Using Convolutional Neural Network Aulia, Rifsya; Safira, Dina Pani; Audilla, Khaury; Raudhatul Khairiyah
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 2: PREDATECS January 2026
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i2.2104

Abstract

Breast cancer ranks among the primary contributors to female mortality, thereby underscoring the critical importance of early detection. This research employs a deep learning approach based on Convolutional Neural Networks (CNNs) to classify breast cancer using ultrasound imagery, comparing the ResNet50V2 and MobileNetV2 architectures with three optimizers: Adam, RMSprop, and SGDM. The dataset used in this study is the Breast Ultrasound Images (BUSI) dataset, obtained from Kaggle, which comprises three diagnostic categories: benign, malignant, and normal. The research workflow encompassed several stages, including data acquisition, image pre-processing involving normalization and augmentation, and dataset partitioning using the Holdout Split method, with proportions of 70% for training, 15% for validation, and 15% for testing. The experimental findings revealed that the ResNet50V2 architecture combined with the SGDM optimizer achieved the best performance, recording accuracy, precision, recall, and F1-score values of 92%. Meanwhile, MobileNetV2 with RMSprop achieved the highest performance on its architecture with 86% accuracy, 88% precision, 86% recall, and 86% F1-score. These findings prove that CNN architecture selection and optimization algorithms have a significant influence on medical image classification performance.
Co-Authors Audilla, Khaury Bintang Aditya Nugroho Nur Alawiyah Hasibuan Raudhatul Khairiyah Safira, Dina Pani