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Contact Name
Ike Yunia Pasa
Contact Email
intek@umpwr.ac.id
Phone
+6282326676888
Journal Mail Official
intek@umpwr.ac.id
Editorial Address
Universitas Muhammadiyah Purworejo Jl. KHA. Dahlan 3 Purworejo Jawa Tengah 54111
Location
Kab. purworejo,
Jawa tengah
INDONESIA
INTEK: Informatika dan Teknologi Informasi
ISSN : 26204843     EISSN : 26204924     DOI : https://doi.org/10.37729/intek.v3i1
Focus and Scopes INTEK Jurnal Informatika dan Teknologi Informasi (INTEK) is a peer-reviewed Journal of Information and Computer Sciences published by Universitas Muhammadiyah Purworejo. This journal publishes two times a year (May and November). INTEK is a media for researchers, academics, and practitioners who are interested in the field of Computer Science and wish to channel their thoughts and findings. The articles contained are the results of research, critical, and comprehensive scientific study which are relevant and current issues covered by the journals. The Scopes of this journal, but not limited to, are: Computer Architecture Parallel and Distributed Computer Pervasive Computing Computer Network Embedded System Human—Computer Interaction Virtual/Augmented Reality Computer Security Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods) Programming (Programming Methodology and Paradigm) Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data) Network Traffic Modeling Performance Modeling Dependable Computing High Performance Computing Bioinformatics Computer Security Human-Machine Interface Stochastic Systems Information Theory Intelligent Systems IT Governance Networking Technology Optical Communication Technology Next Generation Media Robotic Instrumentation Information Search Engine Multimedia Security Computer Vision Information Retrieval Intelligent System Distributed Computing System Mobile Processing Next Network Generation Computer Network Security Natural Language Processing Business Process Cognitive Systems, etc
Articles 1 Documents
Search results for , issue "Vol. 8 No. 2 (2025)" : 1 Documents clear
Kompresi Adaptif untuk Streaming Multimedia Multi-Platform: A Review Ekawati, Nia; Tresnawati, Shandy; Hanief, Shofwan; Saputro, Wahju Tjahjo
INTEK : Jurnal Informatika dan Teknologi Informasi Vol. 8 No. 2 (2025)
Publisher : Program Studi Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/intek.v8i2.7167

Abstract

The current digital era is characterized by cross-platform multimedia consumption, which has become the backbone of information and entertainment, with video content dominating more than 82% of total global internet traffic. The main challenge of adaptive compression for multi-platform multimedia streaming lies in device heterogeneity, network conditions, and the need for Quality of Experience (QoE), as adaptive compression adjusts the bitrate, resolution, and encoding level in real-time. The aim of this research is to explore these issues through a literature review study to identify the scope, taxonomy, gaps, and proposed future research directions. The method used is a literature review covering 51 publications (interval 2019-2025) to examine conceptual aspects, system taxonomy (System Architecture Layer, Adaptation Strategy, Compression Technique, and Artificial Intelligence Model), and to identify research gaps. The review results show that streaming efficiency depends on adaptive compression mechanisms, with a research paradigm shifting from rule-based adaptation toward intelligent adaptive compression. Despite these developments, significant unaddressed gaps remain, particularly concerning synchronization across platforms based on different codecs, low-latency real-time AI compression, robust network adaptation solutions for 5G/B5G, and cross-dimensional integration within a single framework. Future research directions are proposed in platform-specific synchronization models, multi-constraint compression, adaptive AI for slicing, and energy optimization through Federated Learning.

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