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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI
Karim, Muh. Nasirudin
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Computer Science & IT

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Analysis of Splicing Manipulation in Digital Images using Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT) Methods Muhidin, Zumratul; Karim, Muh. Nasirudin; Efendi, Muhamad Masjun
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8540

Abstract

In the digital age, image manipulation is common, often done before publication on social media. However, this can lead to negative impacts, including visual deception. This research aims to detect splicing type image manipulation using Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT) methods. The process starts with image decomposition using DyWT to obtain LL sub-images, followed by local feature extraction using SIFT. An application built on desktop-based Matlab source was developed to detect splicing forgery in digital images. The test used 20 images, this image dataset was taken from canon 5d mark II camera and Vivo X80 mobile phone. Each 10 original images, and 10 edited images. These 10 original images are left as they are without making changes, editing or manipulation, while the other 10 images are changed, edited or manipulated using editing software, the results of this editing are uploaded to social media, such as Facebook and Instagram, which will later be used as datasets in testing. The results show that the splicing technique is detected accurately, and processing is faster on images with low pixel resolution. The DyWT and SIFT methods are effective in detecting post-processing attacks such as rotation and rescaling, although they have drawbacks. DyWT struggles in detecting subtle changes and noise, while SIFT is less effective on non-geometric manipulations. Overall, both methods face challenges in detecting complex manipulations and require significant computational resources, especially on high-resolution images.
Model Infrastruktur Edge Adaptif untuk Smart City dan IoT Berbasis Software Defined Networking Samsumar, Lalu Delsi; Zaenudin, Zaenudin; Supardianto, Supardianto; Karim, Muh. Nasirudin; Muahidin, Zumratul
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 2 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i2.27474

Abstract

 Perkembangan Smart City dan Internet of Things (IoT) menuntut ketersediaan infrastruktur jaringan yang mampu merespons dinamika trafik secara cepat, efisien, dan adaptif. Arsitektur edge konvensional memiliki keterbatasan dalam fleksibilitas, skalabilitas, serta kemampuan pengelolaan sumber daya pada kondisi trafik yang berubah-ubah. Penelitian ini mengusulkan model infrastruktur edge adaptif berbasis Software Defined Networking (SDN) untuk meningkatkan kinerja jaringan pada lingkungan Smart City dan IoT. Model dirancang dengan memanfaatkan pemisahan control plane dan data plane guna memungkinkan orkestrasi jaringan yang dinamis melalui pengaturan jalur, alokasi bandwidth, dan manajemen beban secara real-time. Metode penelitian meliputi perancangan arsitektur, implementasi prototipe menggunakan controller SDN, serta pengujian performa melalui simulasi pada Mininet dengan lima skenario trafik IoT. Evaluasi dilakukan berdasarkan parameter Quality of Service (QoS), yaitu delay, throughput, packet loss, dan jitter. Hasil pengujian menunjukkan bahwa model adaptif berbasis SDN memberikan peningkatan performa signifikan dibandingkan arsitektur edge konvensional, dengan peningkatan throughput sebesar 51,1%, penurunan delay sebesar 35,2%, pengurangan packet loss sebesar 47,4%, serta stabilitas jitter yang lebih baik pada seluruh skenario. Temuan ini menunjukkan bahwa integrasi SDN pada infrastruktur edge merupakan pendekatan efektif untuk mendukung layanan Smart City dan IoT yang bersifat real-time, masif, dan heterogen.
Co-Authors Efendi, Muhamad Masjun Lalu Delsi Samsumar, M.Eng. Muahidin, Zumratul Muhidin, Zumratul Supardianto Supardianto Zaenudin Zaenudin