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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 10 Documents
 1 
Search results for , issue "Vol. 2 No. 6 (2024)" : 10 Documents clear
Integrating Artificial Intelligence in IoT Systems: A Systematic Review of Recent Advances and Application Hamidi, Shir Ahmad; Hashimi, Fareed Ullah; Rahmati, Ajmal
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1420

Abstract

This study explores the intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on contemporary trends, challenges, and emerging applications. The key objectives are assessing the improvements in efficiency, scalability, and automation as a result of AIoT integration, identifying significant challenges realized during implementation, and checking the potential future application in various sectors. A literature review about all aspects was conducted on MDPI, ScienceDirect, IEEE Xplore, and Springer for documents spanning from 2019 to 2024. The review brought to light the significant progress in AIoT: real-time data processing, predictive maintenance, and smart home automation. Core challenges include data security, interoperability, and algorithm manipulation. Future applications using AI on IoT are expected to revolutionize paradigms such as healthcare, smart cities, and agriculture, providing better efficiency and innovation. Newly emerging paradigms from AIoT bear the potential for transformation, emphasizing that related challenges must be adequately tackled for them to result in implementation.
Implementation of an Agent System to Increase Manufacturing Process Efficiency in a Smart Factory Nugroho, Budi; Pasaribu, Hiras; Oscar, Schersclight
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1532

Abstract

The rapid advancement of Industry 4.0 technologies has transformed traditional manufacturing into highly interconnected smart factory systems. However, achieving optimal efficiency in such environments remains challenging due to complex production flows and the need for real-time decision-making. This study explores the implementation of an agent-based system to improve efficiency within a smart factory setting, focusing on how autonomous agents can manage, coordinate, and optimize manufacturing processes. The research aims to analyze the effectiveness of agent systems in reducing production delays, enhancing resource allocation, and improving overall productivity. A combination of simulation and experimental analysis was employed to assess the impact of agent-based solutions on production efficiency. The agent system was integrated into the smart factory model, where agents performed tasks such as process monitoring, predictive maintenance scheduling, and dynamic resource management. Results indicate that the agent system contributed to a 15% reduction in idle time, a 20% improvement in machine utilization, and an overall increase in production throughput. These improvements highlight the potential of agent systems to address inefficiencies in manufacturing by enabling adaptive and autonomous decision-making processes. The findings suggest that agent-based systems are viable solutions for enhancing operational efficiency in smart factories, paving the way for further innovations in automated manufacturing environments. Implementing such systems could lead to more resilient, responsive, and efficient manufacturing processes, ultimately supporting the broader adoption of smart factory practices in the industry.
Implementation of Deep Learning in a Voice Recognition System for Virtual Assistants Apriyanto, Apriyanto; Sahirin, Rohmat; Bradford, Snyder
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1533

Abstract

Voice recognition technology has become a vital component in virtual assistants, enabling more natural and efficient user interactions. However, traditional voice recognition systems face challenges in accurately interpreting diverse accents, dialects, and background noise, which can limit their usability. This study investigates the implementation of deep learning techniques to improve the accuracy and adaptability of voice recognition systems within virtual assistant applications. The research aims to enhance voice recognition performance by leveraging deep learning models that can process complex speech patterns and adapt to varied linguistic nuances. A convolutional neural network (CNN) architecture combined with recurrent neural networks (RNN) was used to train the voice recognition model on a large, diverse dataset of audio samples. The dataset included multiple languages, accents, and noisy environments to test the robustness of the model. Results indicate a 25% improvement in word error rate (WER) and a significant increase in recognition accuracy across diverse voice inputs compared to traditional voice recognition systems. The model demonstrated high adaptability, accurately interpreting speech in varying acoustic conditions, thus improving user experience with virtual assistants. These findings suggest that deep learning can significantly enhance voice recognition systems, offering more reliable performance in real-world applications. Implementing deep learning models in voice recognition systems can bridge the gap between human and machine communication, making virtual assistants more accessible and user-friendly.
Utilization of Multi-Agent Systems in Managing Smart Transportation Systems in Urban Areas Hayati, Amelia; Prasetio, Rachmat; Puspitasari, Mariana Diah; Jiao, Deng
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1534

Abstract

Urban areas face increasing challenges in managing transportation systems due to rising population densities and traffic congestion. Traditional traffic management methods often lack the flexibility and responsiveness needed to address dynamic conditions in real time. This study explores the utilization of multi-agent systems (MAS) as a solution for optimizing smart transportation systems within urban environments. The research aims to evaluate the effectiveness of MAS in improving traffic flow, reducing congestion, and enhancing system responsiveness through autonomous decision-making and coordination among multiple agents. A simulation-based methodology was employed to analyze MAS performance in managing various transportation variables, including traffic density, signal timing, and incident response. Each agent was programmed to perform specific tasks, such as monitoring traffic, optimizing traffic signals, and re-routing vehicles, with collaborative decision-making to address congestion in real time. Results indicate that MAS implementation led to a 30% improvement in traffic flow efficiency and a 25% reduction in congestion levels. The system also demonstrated adaptive capabilities, allowing for real-time adjustments to unexpected conditions, such as accidents or road closures. The findings suggest that multi-agent systems provide a viable, scalable solution for smart transportation management in complex urban settings. Implementing MAS can significantly enhance the efficiency and adaptability of urban transportation systems, contributing to more sustainable and efficient mobility solutions in rapidly growing cities.
Implementation of Grid Computing in Genomic Data Processing in Biomedical Informatics Rahmawati, Rahmawati; Al-Momani, Ammar; Williams, Sarah
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1618

Abstract

The exponential growth of genomic data in biomedical informatics has necessitated efficient computational methods to process and analyze vast datasets. Traditional computational systems often fall short in handling the scale and complexity of genomic data. This study investigates the implementation of grid computing as a scalable and cost-effective solution for genomic data processing in biomedical informatics. The research aims to evaluate the feasibility and performance of grid computing in enhancing data throughput, reducing computational latency, and improving resource utilization in genomic data workflows. The study adopts a methodological approach that integrates grid computing frameworks, such as Globus Toolkit and Apache Hadoop, into genomic data processing pipelines. Simulated genomic datasets and real-world case studies were employed to benchmark the grid computing system against conventional computational environments. The results demonstrate significant improvements in processing speed, with an average reduction of 40% in computational time, and a 25% increase in resource efficiency. Additionally, the system showcased robust scalability, handling up to 10 times larger datasets without compromising accuracy or reliability. In conclusion, the findings underscore the potential of grid computing to revolutionize genomic data processing, making it a pivotal technology in biomedical informatics. This study highlights the importance of adopting distributed computing paradigms to address the challenges posed by modern bioinformatics demands.
Optimization of Grid Computing for Big Data Processing in Biomedical Research Sope, Devi Rahmah; Cale, Wolnough; Aini, M. Anwar; Yusuf, Nur Fajrin Maulana; Zoraida, Masli Nurcahya
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1619

Abstract

The rapid growth of biomedical research has generated massive volumes of data, creating significant computational challenges. Traditional high-performance computing systems struggle to efficiently process, analyze, and manage such large-scale datasets. Grid computing, with its distributed architecture, offers a promising solution by enabling scalable and cost-effective data processing. This study explores the optimization of grid computing frameworks for big data processing in biomedical research, focusing on enhancing computational efficiency, scalability, and fault tolerance. The research aimed to evaluate the performance of optimized grid computing systems in processing diverse biomedical datasets, including genomic, proteomic, and imaging data. A combination of experimental and comparative approaches was employed, integrating grid computing frameworks such as Apache Hadoop and Globus Toolkit with biomedical data pipelines. Key metrics, including processing time, resource utilization, and error rates, were analyzed to assess the system’s performance. The findings demonstrated that optimized grid computing systems reduced processing time by an average of 35% compared to traditional methods while maintaining high accuracy. Scalability tests confirmed the framework’s ability to handle datasets up to 15 times larger without significant performance degradation. Fault tolerance improved through adaptive resource allocation, minimizing workflow interruptions. The study concludes that optimized grid computing is a transformative approach for big data processing in biomedical research. Its ability to enhance computational efficiency and scalability positions it as a crucial tool for addressing the growing data demands of modern biomedical science.
Analysis of the Application of Blockchain in E-Business to Increase Consumer Trust Harjoni, Harjoni; Xavier, Embrechts; Haruka, Hide
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1621

Abstract

The rapid growth of e-business has led to increasing concerns about consumer trust due to issues such as data breaches, fraudulent activities, and lack of transparency. Blockchain technology, with its inherent characteristics of decentralization, immutability, and transparency, has emerged as a potential solution to address these challenges. This research aims to analyze the application of blockchain in e-business to enhance consumer trust, providing insights into its effectiveness and adoption barriers. The study employs a qualitative approach, combining a systematic literature review and expert interviews to gather comprehensive data. The research evaluates blockchain’s impact on key trust factors, such as data security, transaction transparency, and accountability within the e-business ecosystem. The findings reveal that blockchain significantly enhances consumer trust by ensuring data integrity, enabling secure and transparent transactions, and reducing intermediary dependency. However, challenges such as high implementation costs, technical complexity, and regulatory uncertainty hinder widespread adoption. The study concludes that blockchain technology has the potential to revolutionize trust mechanisms in e-business. To maximize its benefits, businesses must address implementation barriers and foster collaborations with regulatory authorities. Future research should explore blockchain’s integration with emerging technologies such as artificial intelligence and the Internet of Things to create a more robust e-business ecosystem.
The Influence of Artificial Intelligence Technology on User Experience in E-Business Haerawan, Haerawan; Mudinillah, Adam; Zou, Guijiao; Anggara, Reddy
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1623

Abstract

The rapid advancement of artificial intelligence (AI) technology has transformed the landscape of e-business, significantly influencing user experience. AI-driven tools such as chatbots, personalized recommendations, and predictive analytics are becoming integral to e-business platforms. Despite widespread adoption, understanding the extent to which AI enhances user satisfaction, engagement, and loyalty remains an area requiring further exploration. This research examines the influence of AI technology on user experience in e-business, focusing on its practical applications and user perceptions. The study adopts a mixed-method approach, combining quantitative surveys with qualitative interviews. Data were collected from 300 e-business users and analyzed to assess key metrics such as usability, efficiency, and satisfaction. Additionally, in-depth interviews with industry experts provided insights into the strategic implementation of AI technologies. The findings reveal that AI significantly enhances user experience by offering personalized interactions, streamlining navigation, and improving response times. Users reported higher satisfaction levels when AI-driven features were implemented effectively. However, concerns about data privacy and algorithmic biases emerged as critical challenges, indicating the need for balanced approaches in AI deployment. The study concludes that while AI technology holds immense potential to revolutionize user experience in e-business, its effectiveness depends on strategic implementation and addressing user concerns. Future research should explore the integration of AI with other emerging technologies to further optimize user interactions in digital environments.
Use of Blockchain for Data Security in E-Government Systems Ridwan, Achmad; Maharjan, Kailie; Ulwi, Krim
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1624

Abstract

The increasing reliance on digital platforms for public administration has heightened concerns about data security in e-government systems. Cyber threats, unauthorized access, and data breaches pose significant risks to the integrity and confidentiality of sensitive governmental information. Blockchain technology, with its decentralized and tamper-proof nature, offers a promising solution for enhancing data security in e-government systems. This research explores the use of blockchain to safeguard data in e-government platforms, focusing on its potential benefits, challenges, and implementation strategies. The study adopts a mixed-method approach, combining a systematic literature review and expert interviews. The literature review analyzed 50 academic articles and industry reports, while interviews with 10 blockchain experts provided practical insights. Key factors such as data integrity, transparency, and access control were evaluated to determine blockchain’s effectiveness in addressing e-government security challenges. The findings reveal that blockchain significantly improves data security by ensuring immutability, enabling secure data sharing, and reducing reliance on central authorities. Experts highlighted blockchain’s potential to enhance transparency and accountability while maintaining privacy through cryptographic techniques. However, challenges such as high implementation costs, scalability issues, and regulatory uncertainties were identified as barriers to adoption. The study concludes that blockchain can revolutionize e-government data security by offering a robust and decentralized framework. Addressing the challenges of implementation and policy alignment will be critical for realizing its full potential. Future research should focus on pilot projects and sector-specific adaptations to accelerate blockchain adoption in e-government systems.
Implementation of a Cloud-Based E-Learning System for Integrated Learning in Higher Education Parini, Parini; Rahmi, Sri Nur; Bili, Fransiskus Ghunu; Ayaka, Ahmya
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1625

Abstract

The integration of technology in higher education has gained significant momentum, with cloud-based e-learning systems emerging as a transformative approach to support integrated and flexible learning environments. Traditional learning systems often face limitations in scalability, accessibility, and resource-sharing, prompting the need for innovative solutions. Cloud-based e-learning systems offer a centralized platform that enhances collaboration, resource management, and learning continuity. This research explores the implementation of a cloud-based e-learning system in higher education institutions, focusing on its impact on learning outcomes and system efficiency. The study employs a mixed-method approach, combining quantitative surveys and qualitative interviews. Data were collected from 300 students and 50 faculty members across three universities that recently adopted cloud-based e-learning platforms. The research assessed system usability, learner engagement, and academic performance, alongside implementation challenges and benefits. The findings reveal that cloud-based e-learning systems significantly improve accessibility, resource-sharing, and collaboration among students and educators. Survey results indicated a 40% increase in learner engagement and a 35% improvement in resource utilization. Faculty interviews highlighted reduced administrative burdens and enhanced flexibility in course delivery. However, challenges such as data security concerns and the need for technical support were noted. The study concludes that cloud-based e-learning systems are a valuable tool for modernizing higher education. Addressing implementation challenges and ensuring continuous technical support are critical for maximizing their potential. Future research should explore long-term impacts and integration with emerging technologies to further enhance learning experiences.

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