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All Journal Scientific Journal of Informatics Journal of Innovation Information Technology and Application (JINITA)
Aradea Aradea
Siliwangi University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Analisis Usability pada Website Jurusan Informatika Universitas Siliwangi Berdasarkan Nielsen Model Mellyana Nur Afifah; Aradea Aradea; Andi Nur Rachman
Jurnal Ilmiah Informatika Vol. 9 No. 1 (2024): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v9i1.90-100

Abstract

Website Jurusan Informatika Universitas Siliwangi memiliki peran penting dalam mendukung aktivitas akademik, administratif, dan penelitian. Namun, beberapa masalah teridentifikasi pada halaman utama, antara lain kurangnya integrasi antara jurusan dan fakultas, penempatan tata letak layanan dan informasi akademik kurang teratur, pengguna kesulitan mencari informasi yang spesifik karena konten yang berbeda digabungkan, adanya musik yang diputar secara otomatis pada saat awal masuk website tanpa fitur pause. Selain itu belum pernah dilakukan penelitian mengenai pengukuran usability pada website Jurusan Informatika dengan metode tertentu kepada pengguna. Tujuan penelitian mengukur tingkat usability pada website Jurusan Informatika Universitas Siliwangi berdasarkan Nielsen Model yang mencakup Learnability, Efficiency, Memorability, Errors, dan Satisfaction, serta memberikan model rekomendasi terkait permasalahan. USE Questionnaire dengan Nielsen model untuk teknik pembuatan pertanyaan, uji validitas data  dengan product moment pearson dan uji reliabilitas data  dengan cronbach's alpha. Analisis data menggabungkan perhitungan SUS dan USE Questionnaire, dan standar kelayakan mengacu pada USE Questionnaire. Hasil penelitian diperoleh nilai learnability sebesar 53%, efficiency 52%, memorability 53%, errors 53%, satisfaction 52% dan usability system 54%. Secara keseluruhan website Jurusan Informatika Universitas Siliwangi mendapatkan rating persentase sebesar 53% atau berada pada kategori “Cukup Layak”. Model rekomendasi difokuskan untuk menata kembali tampilan halaman utama website.
E-SAFE: EfficientNet with Squeeze-and-Attention Feature Enhancement for Deepfake Detection Rianto Rianto; Neng Ika Kurniati; Aradea Aradea; Pandu Pangestu; Irsalina Yumna
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3084

Abstract

Deepfakes have become a serious threat to digital security, individual privacy, and the spread of disinformation globally. The main challenge in detecting manipulated media lies in balancing accuracy, model complexity, and generalization across varying levels of compression. This study proposes the E-SAFE (EfficientNet with Squeeze-and-Attention Feature Enhancement) model, a deepfake detection model integrating the EfficientNet-B0 architecture with the Squeeze-and-Excitation (SE) attention mechanism. This study adopted FaceForensics++ as a benchmark dataset for evaluating deepfake detection. The model was trained with the Adam optimizer and evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and Grad-CAM-based interpretability metrics. Experimental results indicated that E-SAFE attained 95% accuracy, 94% precision, 93% recall, 93% F1-score, and 98% ROC-AUC. The results surpassed the baseline EfficientNet-B0 while maintaining high computational efficiency. These results suggest that integrating the Squeeze-and-Excitation block enhanced the model's sensitivity to subtle facial manipulations without significantly increasing parameter complexity. The E-SAFE model has been shown to be superior in detecting subtle manipulations in deepfake images while maintaining parameter efficiency, thus potentially becoming a reliable solution for multimedia forensics.
Co-Authors Andi Nur Rachman Irsalina Yumna Mellyana Nur Afifah Neng Ika Kurniati Pandu Pangestu Rianto, Rianto