Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal Manajemen Informatika & Teknologi Jurnal Sains Student Research
Muhamad Faqih Febriansyah
Unknown Affiliation
Computer Science & IT Other

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

Found 2 Documents
Search

 1 
Perbandingan Pemanfaatan Algoritma Rekursif dan Iteratif dalam Penyelesaian Struktur Data Pohon Muhamad Faqih Febriansyah; Gunawan; Muhammad Rhamadani; Tata Sutabri
Jurnal Manajemen Informatika & Teknologi Vol. 5 No. 1 (2025): Mei : Jurnal Manajemen Informatika & Teknologi
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/2mxmmg85

Abstract

Tree data structures play a crucial role in computer science and are widely used in applications such as databases, compilers, and file systems. Recursive and iterative algorithms are commonly employed to perform operations on trees, especially in traversal processes like preorder, inorder, and postorder. This study aims to compare the utilization of these two approaches in terms of execution time efficiency, memory usage, and code complexity. The methodology involves testing binary tree traversals with varying node sizes using both recursive and iterative implementations in the Python programming language. Experimental results indicate that recursive algorithms tend to be easier to implement and offer more concise code, but they become less efficient with larger datasets due to system stack limitations. In contrast, iterative algorithms demonstrate more stable performance and better memory efficiency at larger scales, albeit with more complex implementation. Based on these findings, the choice of method should be aligned with application context, dataset size, and available system resources.
KOMPRESI DAN OPTIMASI VIDEO STREAMING BERBASIS AI UNTUK PENGALAMAN PENGGUNAAN MULTIMEDIA YANG LEBIH BAIK Muhamad Faqih Febriansyah; Tata Sutabri
JOURNAL SAINS STUDENT RESEARCH Vol. 3 No. 2 (2025): Jurnal Sains Student Research (JSSR)
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v3i2.4323

Abstract

Video has become a fundamental component in various aspects of modern life. People widely use this medium for a range of purposes, from consuming entertainment content to engaging in online learning activities. However, technical issues related to network infrastructure remain a major challenge. Problems such as high latency, bandwidth fluctuations, and unstable connections often lead to a degraded user experience—ranging from disruptive buffering to sudden drops in video resolution. To address these challenges, researchers have begun developing AI-based approaches for optimizing video compression. Two widely used deep learning architectures are Convolutional Neural Networks (CNNs), which are effective for visual feature extraction, and Generative Adversarial Networks (GANs), which can reconstruct data with high precision. The combination of these techniques enables a significant reduction in video file size without compromising visual quality. Moreover, these systems are designed with adaptive mechanisms that dynamically adjust encoding parameters based on the user’s network conditions. Such implementations allow for more stable video delivery even under limited bandwidth conditions.
Co-Authors Gunawan Muhammad Rhamadani Tata Sutabri