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Journal of Computer Science Advancements
Published by Yayasan Adra Karima Hubbi
ISSN : 30263379     EISSN : 3024899X     DOI : https://doi.org/10.70177/jsca
Core Subject : Science,
Computer Science & IT
Journal of Computer Science Advancements is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the Journal of Computer Science Advancements follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 4 (2025)" : 5 Documents clear
IMPLEMENTATION OF COMPUTER VISION AND NATURAL LANGUAGE PROCESSING IN SOCIAL ROBOTS FOR MORE NATURAL AND INTUITIVE HUMAN-ROBOT INTERACTION Sungkar, Muchamad Sobri; Chirwa, James; Bagrationi, Giorgi
Journal of Computer Science Advancements Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i4.2348

Abstract

The rapid advancement of artificial intelligence (AI) has driven significant developments in social robotics, particularly in enabling more natural and intuitive human-robot interaction (HRI). However, current social robots often struggle to interpret multimodal human input effectively, leading to limited contextual understanding and reduced interaction quality. This study addresses these challenges by integrating computer vision (CV) and natural language processing (NLP) to enhance robots’ perceptual and communicative capabilities. The primary aim is to design and evaluate an interaction framework that allows social robots to recognize human emotions, gestures, and spoken language more accurately, thereby improving the fluency of HRI. A mixed-methods approach was employed, combining experimental implementation with qualitative user studies. The system architecture integrates real-time image recognition, gesture tracking, and speech understanding modules, which were tested through laboratory simulations involving 50 participants in controlled social scenarios. The results demonstrate that robots equipped with CV and NLP modules achieved a 30% improvement in gesture recognition accuracy, a 25% increase in contextual language understanding, and significantly higher user satisfaction scores compared to baseline models. Users reported that the robots exhibited more human-like responsiveness and adaptability in conversational settings. These findings suggest that combining computer vision and NLP substantially improves the naturalness and intuitiveness of human-robot interactions. This research highlights the importance of multimodal AI integration for the next generation of socially intelligent robots and paves the way for applications in healthcare, education, and service industries.
DEVELOPMENT OF A WEB-BASED PURCHASE ORDER SYSTEM IN THE PURCHASING DIVISION USING THE AGILE MODEL (CASE STUDY: CV KLAMBY) Mawarni, Fauziah Ika; Razi, Fahrul
Journal of Computer Science Advancements Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i4.2412

Abstract

This study aims to design and develop a web-based purchase order (PO) system equipped with an integrated approval feature by implementing the Agile development methodology. Agile is chosen for its iterative, flexible, and user-oriented development approach. The research follows several stages, including planning, requirements analysis, system design, development, testing, implementation, and evaluation. The system was developed using JavaScript (Node.js) and MySQL, and tested through blackbox testing. The results show that the system effectively facilitates PO creation, vendor and product data management, and supports secure, automated multi-level approval. The implementation of this system has been proven to enhance the efficiency and accuracy of procurement processes, reduce human error, and provide better-organized documentation. This system is expected to serve as a digital solution that can be adopted by other companies and strengthen the application of Agile methodology in information system development projects.
GOODBYE LATENCY: WHY FUTURE MEDICAL DEVICES NEED ARTIFICIAL BRAINS Koh, Megan; Tan, Marcus; Wong, Lucas
Journal of Computer Science Advancements Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i4.3332

Abstract

The transition of medical technology from passive monitoring to autonomous, closed-loop intervention is critically impeded by the latency and power inefficiencies of traditional Von Neumann computing architectures. This study investigates the efficacy of neuromorphic hardware as a solution, aiming to validate a bio-inspired architecture capable of sub-millisecond decision-making for life-critical applications. Employing a rigorous hardware-in-the-loop simulation framework, we benchmarked a custom Spiking Neural Network (SNN) against industry-standard microcontrollers, utilizing large-scale cardiac and neurological datasets to evaluate inference speed, energy consumption, and signal fidelity. Quantitative results reveal that the neuromorphic system achieved a 94% reduction in end-to-end latency and a thirty-eight-fold improvement in energy efficiency compared to the digital baseline. The event-driven architecture successfully maintained 96.4% diagnostic accuracy while operating within a negligible thermal envelope suitable for implantation. These findings definitively establish that mimicking biological asynchronous processing eliminates fatal temporal delays, validating neuromorphic “artificial brains” as the essential technological foundation for the next generation of responsive, privacy-secure, and energy-autonomous medical implants.
FUTURE DATA CENTERS: LIQUID IMMERSION COOLING INNOVATION TO WITHSTAND AI HEAT Thai, Aom; Krit, Pong; Lek, Siri
Journal of Computer Science Advancements Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i4.3333

Abstract

The exponential escalation of computational density required by modern Artificial Intelligence (AI) and Large Language Models has pushed traditional air-cooled data center infrastructures to their thermodynamic limits. This study investigates the efficacy of single-phase liquid immersion cooling as a transformative solution to manage the extreme thermal flux of next-generation AI accelerators. Adopting a quantitative experimental design, we benchmarked a high-density GPU cluster submerged in a proprietary dielectric fluid against a standard forced-air baseline under intensive MLPerf training workloads. The research focused on evaluating key performance indicators, including Power Usage Effectiveness (PUE), processor junction temperatures, and total energy consumption over a 168-hour stress test. Results demonstrate that the immersion architecture achieved a near-ideal PUE of 1.04, representing a 34% efficiency improvement over the air-cooled control group. Furthermore, the liquid medium maintained GPU core temperatures 20°C lower than the baseline, effectively eliminating thermal throttling events and enhancing computational stability. The study concludes that shifting from aerodynamic to hydrodynamic cooling is not merely an efficiency upgrade but a physical prerequisite for the sustainable scaling of exascale AI infrastructure, offering a viable pathway to decarbonize the expanding digital economy.
ENCRYPTION APOCALYPSE? PREPARING DATA SECURITY FOR THE QUANTUM COMPUTING ERA Iqbal, Kiran; Ali, Zainab; Aslam, Bilal
Journal of Computer Science Advancements Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i4.3338

Abstract

The imminent maturation of quantum computing threatens to nullify the mathematical hardness underpinning global Public Key Infrastructure, creating an urgent “Harvest Now, Decrypt Later” vulnerability. This study investigates the operational feasibility of transitioning to NIST-standardized Post-Quantum Cryptography (PQC) protocols within heterogeneous network environments. Utilizing a rigorous quantitative benchmarking framework, we evaluated the performance of lattice-based primitives, specifically ML-KEM and ML-DSA, against classical standards across high-performance servers and resource-constrained IoT devices. Empirical data reveals a fundamental architectural paradigm shift: while PQC algorithms exhibit superior computational execution speeds, they introduce severe transmission overheads, resulting in memory saturation and packet fragmentation on edge hardware. Results demonstrate that hybrid encryption schemes provide valid risk mitigation but incur statistically significant latency penalties due to expanded artifact sizes. We definitively conclude that the “Encryption Apocalypse” is primarily a bandwidth and memory bottleneck rather than a computational one, mandating the immediate deployment of adaptive crypto-agility frameworks to manage the infrastructural constraints of the post-quantum era.

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