Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Djtechno: Jurnal Teknologi Informasi Jurnal Minfo Polgan (JMP)
Afla Nevrisa
Politeknik Negeri Lhokseumawe
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Mathematics Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
IMPLEMENTASI RANDOM EARLY DETECTION PADA JARINGAN WIRELESS UNTUK MENINGKATKAN QUALITY OF SERVICE afla nevrisa
Djtechno: Jurnal Teknologi Informasi Vol 7, No 1 (2026): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i1.8651

Abstract

Peningkatan penggunaan jaringan nirkabel menimbulkan tantangan dalam menjaga kualitas layanan (QoS), terutama saat kepadatan trafik menyebabkan delay, jitter, packet loss, dan penurunan throughput. Penelitian ini bertujuan mengevaluasi efektivitas metode Random Early Detection (RED) dalam meningkatkan QoS pada jaringan wireless dengan beban tinggi. Pengujian dilakukan melalui enam skenario aktivitas klien, yaitu speedtest, download, upload, serta streaming YouTube resolusi standar dan 4K. Perangkat yang digunakan meliputi MikroTik, Access Point TP-Link, tiga laptop, dan dua HP, dengan pemantauan menggunakan Wireshark. Parameter QoS yang dianalisis mencakup delay, jitter, throughput, dan packet loss. Hasil menunjukkan bahwa RED mampu menurunkan delay dan jitter secara signifikan, seperti pada skenario download dari 372,14 ms menjadi 50,71 ms dan jitter dari 349,14 ms menjadi 50,75 ms. Meskipun throughput sedikit menurun akibat mekanisme packet drop, kestabilan jaringan tetap terjaga dengan packet loss 0% di seluruh skenario. Selain itu, RED mampu melakukan packet drop secara selektif saat antrian melebihi ambang batas. Dengan demikian, RED efektif sebagai solusi manajemen trafik untuk meningkatkan kinerja jaringan wireless pada kondisi padat dan multi-client aktif.Kata Kunci: Delay, Jitter, Quality of Service, Random Early Detection, wireless
Penerapan Real-ESRGAN untuk Restorasi dan Rekompresi Arsip Penyiaran Video Resolusi Standar Umri Erdiansyah; Novira Dwina; Afla Nevrisa; Hosea Sitepu
Jurnal Minfo Polgan Vol. 15 No. 3 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i3.16521

Abstract

The modern broadcasting industry requires visual content in high-definition (HD) resolution. This technological transition creates critical issues in digital asset management, specifically regarding legacy broadcasting archives that are primarily recorded in standard definition (SD) formats. Conventional spatial interpolation approaches fail to address this issue, producing blurry and pixelated images. The application of artificial intelligence, particularly the Real-ESRGAN algorithm, offers a promising restoration solution. However, this implementation significantly increases the raw file size, making it impractical for local server storage. This research aims to find a precise equilibrium between visual quality enhancement using AI and storage efficiency through High-Efficiency Video Coding (HEVC) compression. This study uses a quantitative experimental method via laboratory-scale software engineering. The intervention phases include pixel reconstruction using the AI model and file size reduction using the HEVC standard. Data collection involved ten SD video samples. Real-ESRGAN qualitatively restores texture details and removes analog noise without excessive artifacts. Due to file size expansion, further compression using HEVC with varying Constant Rate Factor (CRF) parameters is being conducted to reduce the size by over 50% without severe Video Multimethod Assessment Fusion (VMAF) metric degradation. The integration of AI upscaling and HEVC compression creates a highly applicable workflow for television industry needs.
Co-Authors Hosea Sitepu Novira Dwina Umri Erdiansyah