Journal of Computer Science Advancements

EDGE COMPUTING AND REAL-TIME DATA PROCESSING: OPTIMIZING LATENCY AND EFFICIENCY IN INTERNET OF THINGS (IOT) ECOSYSTEMS

2026-04-07T06:42:47+07:00

The rapid expansion of Internet of Things (IoT) ecosystems has intensified the need for efficient real-time data processing, exposing limitations of cloud-centric architectures in handling latency-sensitive applications. Increasing data volumes, network congestion, and delayed response times have highlighted the necessity of decentralized computing approaches. This study aims to examine the effectiveness of edge computing in optimizing latency and system efficiency within IoT environments. A mixed-methods experimental and simulation-based design was employed, comparing edge-based, cloud-based, and hybrid architectures across multiple application scenarios. Performance metrics including latency, throughput, energy consumption, and bandwidth utilization were analyzed using statistical and comparative techniques. Findings indicate that edge computing significantly reduces latency and energy consumption, while hybrid architectures achieve optimal throughput and scalability. Bandwidth utilization emerges as a key mediating factor influencing system performance, with decentralized processing improving responsiveness under high network load conditions. The study concludes that edge computing provides a robust and adaptive solution for enhancing real-time data processing in IoT ecosystems, particularly when integrated with cloud systems through optimized task allocation strategies. Effective deployment requires context-aware design, efficient resource management, and alignment with application-specific requirements.

OPTIMIZATION OF KSM MAJU JAYA OPERATIONS THROUGH INFORMATION TECHNOLOGY INNOVATION FOR DATA MANAGEMENT AND SALES OF ENVIRONMENTALLY FRIENDLY PRODUCTS

2026-04-14T01:29:09+07:00

Community-based waste management through Kelompok Swadaya Masyarakat (KSM) Maju Jaya plays a vital role in the circular economy by transforming organic refuse into eco-friendly products like natural dish soap. Manual recording systems and conventional marketing strategies currently hinder operational efficiency, leading to data inaccuracies and limited market reach. This research aims to optimize KSM operations by developing an integrated web-based management information system focused on data management and sales. The study employs a Research and Development (R&D) methodology, incorporating user requirement identification, iterative UI/UX prototyping using Figma, and functional verification through blackbox testing. Results demonstrate that the developed system provides a systematic, transparent, and accurate platform for tracking production, stock, and transactions. The web-mobile responsive design ensures accessibility across various devices, significantly reducing human error and accelerating report generation. This research concludes that digital transformation through user-centric information technology effectively enhances the institutional capacity and sustainability of community-led environmental initiatives. Implementation of this system serves as a scalable model for other grassroots organizations transitioning toward professionalized digital management.

SYSTEMATIC REVIEW OF THE UTILIZATION OF ARTIFICIAL INTELLIGENCE IN FORENSIC DENTISTRY AS A ROLE MODEL FOR IMPLEMENTATION AT RSAL DR MINTOHARDJO

2026-04-07T08:52:57+07:00

Forensic odontology plays a critical role in human identification, yet conventional methods remain time-consuming, subjective, and limited in handling large-scale data, particularly in disaster and military contexts. This study aims to systematically review the utilization of artificial intelligence in forensic odontology and to develop a contextual role model for implementation at RSAL dr Mintohardjo. A systematic review design was employed by analyzing peer-reviewed articles from major databases published between 2014 and 2025 using predefined inclusion criteria and thematic synthesis. Findings indicate that artificial intelligence, especially deep learning models, significantly improves accuracy, efficiency, and scalability in dental identification, age estimation, and bite mark analysis, with performance often exceeding ninety percent under controlled conditions. Results further reveal that successful implementation depends on data quality, interdisciplinary collaboration, and institutional readiness, while challenges include ethical concerns, data limitations, and lack of standardized protocols. The study concludes that artificial intelligence has strong potential to transform forensic odontology practices and can serve as a strategic role model for institutional adoption, provided that technological integration is aligned with infrastructure, human resources, and governance frameworks. Implications extend to policy development, capacity building, and future research directions emphasizing real-world validation and sustainable implementation strategies in complex healthcare environments globally