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All Journal Jurnal Masyarakat Informatika
Baihaqi, Muhamad Nur
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Computer Science & IT

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Image Encryption and Decryption Using Vigenere Cipher with Compute Unified Device Architecture (CUDA) Kusuma, Arjuna Wahyu; Damanhuri, R.; Baihaqi, Muhamad Nur; Sanjaya, Labib Habibie
Jurnal Masyarakat Informatika Vol 14, No 1 (2023): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.14.1.51670

Abstract

Compute Unified Device Architecture (CUDA) adalah Application Programming Interface (API) NVIDIA dan platform yang memungkinkan akses langsung ke set instruksi GPU dan memberi dukungan untuk berinteraksi dengan GPU terkait komputasi paralel. Dengan CUDA, komputasi yang kompleks menjadi lebih cepat dan lebih efisien. Vigenere Cipher adalah kriptografi klasik populer yang mengimplementasikan kunci simetris dengan panjang tertentu. Pada penelitian ini, penerapan enkripsi dan dekripsi Vigenere Cipher dilakukan pada citra serta dengan CPU dan GPU (CUDA). Paralelisasi dengan CUDA menunjukkan hasil eksekusi waktu yang relatif lebih cepat daripada CPU. Persentase rata-rata penurunan waktu adalah 99,46 persen untuk enkripsi serta 99,47 persen untuk dekripsi.Persentase rata-rata penurunan waktu adalah 99,46 persen untuk enkripsi serta 99,47persen untuk dekripsi.
Classification of Real and Fake Images Using Error Level Analysis Technique and MobileNetV2 Architecture Baihaqi, Muhamad Nur; Sugiharto, Aris; Tantyoko, Henri
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.73283

Abstract

Advancements in technology have made image forgery increasingly difficult to detect, raising the risk of misinformation on social media. To address this issue, Error Level Analysis (ELA) feature extraction can be utilized to detect error level variations in lossy-formatted images such as JPEG. This study evaluates the contribution of ELA features in classifying authentic and forged images using the MobileNetV2 model. Two scenarios were tested using the CASIA 2.0 dataset: without ELA and with ELA. Fine-tuning was performed to adapt the model to the new problem. Experimental results show that incorporating ELA improves model accuracy up to 93.1%, compared to only 76.41% in the scenario without ELA. Validation using k-fold cross-validation yielded a high average f1-score of 96.83%, confirming the effectiveness of ELA in enhancing the classification performance of authentic and forged images.
Co-Authors Aris Sugiharto Damanhuri, R. Kusuma, Arjuna Wahyu Sanjaya, Labib Habibie Tantyoko, Henri