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All Journal JUITA : Jurnal Informatika INOVTEK Polbeng - Seri Informatika
Rahman, Salsabilah Aulia
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Computer Science & IT

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Perbandingan Algoritma Weighted Least Connection dan Weighted Round Robin pada Load Balancing Berbasis Docker Swarm Rahman, Salsabilah Aulia; Hadiwandra, T. Yudi
Jurnal Inovtek Polbeng Seri Informatika Vol 8, No 2 (2023)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v8i2.3395

Abstract

Perkembangan teknologi telah mengubah kebiasaan menonton televisi di Indonesia dengan peralihan dari televisi analog ke digital dan popularitas layanan video on demand. Namun, peningkatan penggunaan video on demand membawa tantangan dalam menjaga performa server yang harus melayani permintaan tinggi. Solusinya adalah menerapkan teknik load balancing dan algoritma penjadwalan yang efisien, seperti menggunakan docker swarm sebagai teknologi virtualisasi container. Penelitian ini menguji 3 kategori resolusi video 240p, 720p, dan 1080p serta variasi permintaan request dari 1 hingga 10.000. Hasil penelitian menunjukkan bahwa semakin besar resolusi video, ukuran file video juga semakin besar, yang mengakibatkan penurunan throughput dan peningkatan response time, request loss, serta CPU Utilization. Hasil analisis menunjukkan bahwa algoritma weighted least connection lebih unggul daripada weighted round robin berdasarkan parameter yang ditentukan. Implementasi docker swarm berdampak positif dalam menjaga performa server dibandingkan dengan menggunakan single server. Penelitian ini memberikan wawasan mengenai penerapan teknologi dan algoritma dalam meningkatkan performa server video on demand serta memastikan pengalaman menonton yang optimal bagi pengguna. 
Comparing GAN, Diffusion, and Diffusion-GAN for Single-Image Deraining of UAV Imagery Rahman, Salsabilah Aulia; Rahadianti, Laksmita
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

Single-image deraining for Unmanned Aerial Vehicle (UAV) imagery remains challenging due to non-uniform rain patterns, motion blur, and real-time processing requirements. Existing generative paradigms, including Generative Adversarial Networks (GAN), Diffusion, and Diffusion–GAN, each face inherent trade-offs among restoration quality, stability, and efficiency. To address the lack of unified and fair benchmarking across these paradigms, this study presents a systematic and controlled comparative evaluation of three representative models, including TBGAN, WeatherDiff, and SupResDiffGAN, to assess their relative performance in UAV deraining tasks. The models are evaluated on the UAV-Rain1K and Rain100L datasets using PSNR, SSIM, and inference efficiency metrics to support informed selection of paradigms for UAV applications. Experimental results show that WeatherDiff achieves the highest fidelity with 19.99 dB PSNR, 0.8375 SSIM on UAV-Rain1K and 29.51 dB PSNR, 0.9093 SSIM on Rain100L. TBGAN yields sharper details but lower structural consistency, whereas SupResDiffGAN offers balanced performance with 19.03 dB PSNR and 0.7053 SSIM on UAV-Rain1K and 28.51 dB PSNR and 0.8681 SSIM on Rain100L, with faster inference. These findings highlight the practical trade-offs among the three paradigms and demonstrate that diffusion–GAN frameworks provide the most practical solution for UAV deraining, combining diffusion stability with adversarial sharpness for real-time restoration.
Co-Authors Hadiwandra, T Yudi Rahadianti, Laksmita