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Scientific Journal of Computer Science
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31103170     DOI : https://doi.org/10.64539/sjcs
Core Subject : Science,
Computer Science & IT
The Scientific Journal of Computer Science (SJCS) (e-ISSN: 3110-3170) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The SJCS dedicated to publishing high-quality research across all areas of computer science, with a particular focus on emerging technologies that are shaping the future of computing. SJCS invites original research, review papers, and studies that involve practical applications, simulations, and theoretical advancements. The journal scope includes, but is not limited to: Artificial Intelligence and Machine Learning Data Science and Big Data Cybersecurity and Cryptography Cloud Computing and Distributed Systems Software Engineering Human-Computer Interaction Computer Vision and Natural Language Processing Internet of Things (IoT) Blockchain Technologies Robotics and Automation Computational Biology and Bioinformatics All fields related to computer science SJCS aims to advance the development of innovative computing systems that contribute to technological progress across industries.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2025): July" : 5 Documents clear
Bandwidth Management Using the Hierarchical Token Bucket Method to Enhance Server Network Performance Ahmad Jayadi; Dedi Satriawan Kusnayadi; Syahrani Lonang; Abdennasser Dahmani; Zied Driss; Abdel-Nasser Sharkawy
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): July
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.40

Abstract

Villa Nomada, as an accommodation in Kuta, Central Lombok, is experiencing internet network instability due to uneven and uncontrolled bandwidth distribution, which disrupts user comfort, especially for foreign guests who require an optimal connection. The solution implemented is bandwidth management using the Hierarchical Token Bucket (HTB) method to allocate bandwidth fairly and efficiently. This research contributes to improving quality of service (QoS) by optimizing network performance through HTB. The method used is HTB configuration to allocate bandwidth based on user categories (VIP, Regular, and Office). Network performance was evaluated before and after implementation to measure improvements in speed and stability. The research results showed that HTB successfully distributed bandwidth evenly, with VIP users receiving priority, while regular and office users obtained stable connections without interruptions. Network efficiency improved, reducing congestion and increasing user satisfaction. We rated the HTB method as “Good” for optimizing network performance. In conclusion, the implementation of HTB successfully addressed the bandwidth management issues at Villa Nomada, ensuring fair distribution and optimal network performance for all users.
A Statistical Approach to Crime Rate Prediction Using Multiple Linear Regression Setiawan Ardi Wijaya; Edo Arribe; Muhitualdi
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): July
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.47

Abstract

The high crime rate in Riau Province poses a serious threat to social stability and public safety, requiring accurate prediction strategies to support crime prevention efforts. Based on data from the Central Statistics Agency (BPS), Riau ranked seventh among the provinces with the highest crime rates in Indonesia in 2022, indicating that conventional prevention efforts remain insufficient. However, studies applying statistical data-based prediction models to crime in Riau are still limited, creating a gap in data-driven decision making. This study aims to develop a crime rate prediction model in Riau Province using the Multiple Linear Regression (MLR) method with BPS crime data from 2019–2023. The independent variables include six types of crime: corruption, drug dealers, drug users, terrorism, illegal logging, and human trafficking, while the dependent variable is the total number of crimes per district or city. The research process involved data collection, understanding, preprocessing, application of linear regression algorithms, model training and testing, and evaluation using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that Pekanbaru City recorded the highest number of cases, mostly related to drug crimes. The model predicts an increase in Pekanbaru’s cases from 3,331 in 2024 to 5,852 in 2027, while Dumai City is projected to decline from 543 to 397 cases. The model demonstrates high accuracy in most areas, particularly in Kampar (MAPE 0.28%), Siak (0.52%), and Rokan Hilir (0.94%), though less accurate in the Meranti Islands (565.99%) due to data instability. These findings prove that the Multiple Linear Regression method effectively predicts crime trends and can serve as a quantitative decision-making tool for law enforcement and local governments. Further research should include socioeconomic factors such as poverty and unemployment, and compare results with alternative forecasting methods like ARIMA and Exponential Smoothing to enhance prediction accuracy.
A GAT-Assisted Hybrid Reinforcement Learning and Swarm Intelligence Framework for Autonomous UAV Coordination Mst Jannatul Kobra; Md Owahedur Rahman; Mizanur Rashid
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): July
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.316

Abstract

The autonomous UAV swarms have fundamental issues with strong coordination that arise under delays in communication, dynamic obstacles and noisy sensing environments, and the existing centralized or heuristic-based solutions are insufficient in addressing such issues. To cover this gap, this paper proposes a Graph Attention Network (GAT)-based Hybrid Reinforcement Learning and Swarm Intelligence Framework that can enable the communication-aware decentralized cooperation of UAVs. It is a multi-agent reinforcement learning and PSO, ACO, Differential Evolution, flocking behavior and Control Barrier Function-based safety correction, and GAT-inspired adaptive graph communication encoding. The results of the simulation of 18 episodes with 24 UAVs demonstrate that the reward, coverage, and collision were demonstrated to be improved by 32%, 27%, and 40% respectively as compared to a classical greedy baseline. The findings confirm the fact that the proposed hybrid GAT-RL architecture enables to promote significantly more scalability, safety, and real-time responsiveness of UAV swarms, which is a possibility on the path to large-scale autonomous aerial coordination.
Model Context Protocol: The Central Nervous System for a New Generation of Artificially Intelligent Agents Joydeep Sarkar; Gopichand Agnihotram
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): July
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.327

Abstract

As multi-agent AI systems evolve from prototypes to production-grade enterprise applications, the need for a robust and scalable communication architecture has become an operational imperative. However, traditional approaches like linear chaining or ad-hoc peer-to-peer messaging result in brittle, unmanageable systems that lack observability and fail to handle the non-deterministic nature of AI agents. To address this architectural deficiencies, this paper introduces the Message, Context, and Protocol (MCP) framework, an architectural pattern designed to serve as a communication backbone and "central nervous system" for complex AI systems by decoupling agent intent from execution. Performance evaluations under simulated enterprise load demonstrate that by decoupling agents through a central message bus and a stateful orchestrator, MCP maintains system resilience and prevents catastrophic failure even under high load (500 req/sec), although the orchestrator itself is identified as a primary bottleneck requiring horizontal scaling. These results underscore that centralized state management is not merely an option but a necessity for enterprise AI, providing the modularity and fault tolerance required to transition agentic workflows from experimental concepts to reliable business solutions.
An Ensemble-based Adaptable and Privacy-aware Threat Detection Mechanism for Wireless Sensor Network in Healthcare Systems Emmanuel Iheanacho Afonne; Patrick Ejeh; Linda Chioma Aworonye
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): July
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.328

Abstract

Recently, wireless sensor networks (WSNs) have been widely integrated in critical applications such as environmental monitoring, smart cities, and modern healthcare for remote patient monitoring and data collection. This makes WSNs increasingly susceptible to security threats, including eavesdropping, jamming, sybil, data injection, routing, senor node capture, malicious intrusion attacks etc., therefore maintaining integrity, confidentiality, and availability of sensitive data and preserving privacy become a challenge. Existing mechanisms do not integrate threat detection, privacy preservation, and adaptability to evolving threats leading to security breaches in the left-out security requirements. This paper proposes an ensemble-based threat detection mechanism (FAL-ELeM-IDS) with privacy-awareness and adaptability to evolving threats for WSNs-based healthcare systems. The ensemble consists of Online Random Forest, Online AdaBoost, Support Vector Machine, Neural Network, and XGBoost to ensure detection high accuracy and low false-positives. Federated Learning combined with ensemble technique to provide confidentiality and a combined Online Adaptive Boosting and Online Random Forests algorithms to provide adaptability. The proposed model trained on a real-world healthcare sensor dataset demonstrates its superiority in performance compared to conventional models. An accuracy of 97.8%, a recall of 97%, precision of 98%, and F1-score of 97.5%, was achieved outperforming individual models by significant margins, showing that the model is accurate and reliable in detecting threats. This mechanism implies enhanced system security and privacy, timely threat mitigation ensuring patient safety, and boost in public acceptance for sensor-based healthcare services. Overall, this work contributes a scalable, privacy-aware, and adaptive threat detection mechanism suitable for integration in the sensitive healthcare applications.

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