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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Rahmadwati
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Skema Digital Watermarking Citra dengan Metode TLDCT dan Chinese Remainder Theorem Danang Aditya Nugraha; Rahmadwati; Muhammad Aziz Muslim
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 2: Mei 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1719.404 KB)

Abstract

Currently, it is easy to access the wide variety of digital content on the internet, and therefore protection effort for digital content is an important problem that needs to solve. One kind of digital content that urgently needs to protect is digital image. Protection is needed not only for the copyright but also for the authenticity of digital image because there are so many advanced image editing software, that are easy to use. This paper presents a scheme for digital image protection through Digital Watermarking process using Two Level Discrete Cosine Transform, and Chinese Remainder Theorem for color digital image. This scheme has an ability to detect and to recover some kind of changes that occur in the watermarked image. This ability can be achieved because this scheme is categorized into fragile digital watermarking, thus, has low robustness. However, based on experiment results, this scheme shows a good invisibility. It is higher than the previous research and has a minimum distortion, proven by Peak Signal to Noise Ratio (PSNR) value of around 45.
Identification of BSR Disease in Oil Palm Using UAV Imagery through CNN and SCNN Approaches Zakia Azzahro; Rahmadwati; Angger Abdul Razak; Amrul Faruq
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2546

Abstract

Basal Stem Rot (BSR) disease caused by Ganoderma boninense is a major threat to oil palm productivity due to its destructive nature and the challenges associated with early-stage detection. To support sustainable production and mitigate significant yield losses, a system capable of classifying oil palm trees into healthy and infected categories is required. In this study, two deep learning approaches, namely CNN and SCNN, are applied to identify oil palm conditions based on UAV-derived imagery. While CNN is widely used for image-based detection tasks due to its capability to extract relevant visual representations, it is prone to overfitting during training. Therefore, SCNN is employed to address this issue by leveraging image similarity comparison mechanisms. Experimental results show that both methods achieve high classification accuracy, with SCNN outperforming CNN by achieving an accuracy of 96.48% compared to 95.644% for CNN. The superior performance of SCNN indicates its sensitivity to subtle visual differences between healthy and early-stage infected oil palm trees, enabling more reliable classification performance. Thus, SCNN is considered more effective for oil palm condition detection and contributes to reducing overfitting, resulting in improved model stability.
Co-Authors Amrul Faruq Angger Abdul Razak Danang Aditya Nugraha Muhammad Aziz Muslim Zakia Azzahro