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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science Jurnal Testing dan Implementasi Sistem Informasi
Safira, Dina Pani
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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 Kepuasan Pengguna Pada Layanan Akademik Menggunakan Metode Rapid Application Development Safira, Dina Pani; Chaniago, M. Eric; Sandi, Kelvin Mei; Hasibuan, Radja Ardhiansyah
Jurnal Testing dan Implementasi Sistem Informasi Vol. 2 No. 2 (2024): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v2i2.907

Abstract

Dalam standar ISO 9001:2015 klausul 9.1.2, organisasi diwajibkan untuk memantau persepsi pelanggan untuk memastikan kebutuhan dan harapan mereka terpenuhi. Universitas Islam Negeri Sultan Syarif Kasim Riau menggunakan Google Form untuk mengumpulkan data kepuasan pengguna terhadap layanan administrasi. Namun, berdasarkan survei, sebagian besar responden menyatakan bahwa Google Form memiliki keterbatasan dalam desain, format, keamanan, dan transparansi data. Untuk mengatasi permasalahan ini, penelitian ini mengusulkan pembangunan sistem kepuasan pengguna yang lebih terstruktur dan sistematis. Metode Rapid Aplication Development (RAD) digunakan untuk membuat sistem ini menjadi aplikasi web yang menggunakan PHP dan MySQL. Hasil pengujian menunjukkan bahwa sistem ini berhasil memenuhi persyaratan fungsional dan non-fungsional di Universitas Islam Negeri Sultan Syarif Kasim Riau dan mampu meningkatkan kualitas layanan administrasi.
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 Aulia, Rifsya Chaniago, M. Eric Hasibuan, Radja Ardhiansyah Raudhatul Khairiyah Sandi, Kelvin Mei