cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Abdul Aziz
Contact Email
abdulazizbinceceng@gmail.com
Phone
+6282180992100
Journal Mail Official
journaleastasouth@gmail.com
Editorial Address
Grand Slipi Tower, level 42 Unit G-H Jl. S Parman Kav 22-24, RT. 01 RW. 04 Kel. Palmerah Kec. Palmerah Jakarta Barat 11480
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
The Eastasouth Journal of Information System and Computer Science
Published by Eastasouth Institute
ISSN : 30266041     EISSN : 3025566X     DOI : https://doi.org/10.58812/esiscs
Core Subject : Science,
Computer Science & IT
ESISCS - The Eastasouth Journal of Information System and Computer Science is a peer-reviewed journal and open access three times a year (April, August, December) published by Eastasouth Institute. ESISCS aims to publish articles in the field of Enterprise systems and applications, Database management systems, Decision support systems, Knowledge management systems, E-commerce and e-business systems, Business intelligence and analytics, Information system security and privacy, Human-computer interaction, Algorithms and data structures, Artificial intelligence and machine learning, Computer vision and image processing, Computer networks and communications, Distributed and parallel computing, Software engineering and development, Information retrieval and web mining, Cloud computing and big data. ESISCS accepts manuscripts of both quantitative and qualitative research. ESISCS publishes papers: 1) review papers, 2) basic research papers, and 3) case study papers. ESISCS has been indexed in, Crossref, and others indexing. All submissions should be formatted in accordance with ESISCS template and through Open Journal System (OJS) only.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 122 Documents
 9   10   11   12   13 
APEX: Adaptive Personal EXperience Agents A Cost-Efficient, Privacy-Preserving Architecture for Scalable AI Assistants Pareek, Abhishek; Misra, Udit; Chukkapalli, Divya
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i03.935

Abstract

Personal AI agents deployed on user devices operate under fundamentally different constraints than shared cloud services. These systems must maintain conversation context across extended periods, function efficiently despite irregular usage patterns, handle complex requests, allocate computation intelligently, and protect sensitive data. We present APEX, an architecture addressing these five challenges through integrated design. APEX comprises five technical contributions: (1) a hierarchical memory system achieving 84% storage reduction through progressive compression; (2) a predictive activation mechanism reducing per-user compute costs by 73% while maintaining sub-5-second startup latency; (3) a task decomposition engine with 94% end-to-end accuracy; (4) a cost-aware routing layer reducing API consumption by 61%; (5) federated personalization enabling on-device learning while preserving privacy. Six-month production deployment reduced per-user monthly costs from $156 to $42 with positive user satisfaction scores, demonstrating practical efficiency at scale.
Decoupling the Core: A Technical Roadmap for Modernizing Mainframe into Cloud-Native Microservices on Azure Kubernetes Service Roy, Tathagata
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.1012

Abstract

Modernizing tightly coupled mainframe ecosystems built on COBOL business logic, CICS transaction flows, CA7 batch schedules, and DB2/VSAM data structures remains a technically complex undertaking for enterprises seeking cloud‑native agility. This paper presents a structured roadmap for decomposing monolithic mainframe workloads into Spring Boot microservices deployed on Azure Kubernetes Service, supported by Kafka for asynchronous, event‑driven communication. The work examines the translation of COBOL Copybooks into JSON or Avro schemas for API‑driven interoperability, the extraction of COBOL and stored‑procedure logic into Spring Data and service layers, and the re‑engineering of JCL utilities into containerized batch pipelines orchestrated through Kubernetes‑native schedulers. It also evaluates approaches for transforming 3270 CICS interfaces into modern React or Spring Thymeleaf frontends, migrating IBM MQ patterns into Kafka‑aligned messaging, and integrating RACF‑based security models with cloud identity providers. Automated code‑analysis and refactoring tools available at the time of writing are assessed for their ability to accelerate large‑scale modernization while preserving transactional integrity and regulatory compliance. The resulting roadmap provides a technically grounded strategy for decoupling mainframe cores and transitioning toward resilient, modular, and cloud‑aligned architectures.
Blockchain-Integrated System Architecture for Secure and Transparent Data Transactions Wajihi Ali; Md. Kamal Khan; Walid Nguia; Okouma Nguia
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.1042

Abstract

The growing demand for secure, transparent, and efficient data transaction systems has highlighted the limitations of traditional centralized architectures, particularly in terms of data integrity, trust, and vulnerability to cyber threats. This study proposes a blockchain-integrated system architecture designed to enhance the security and transparency of data transactions. The proposed framework combines layered system design with decentralized blockchain technology, incorporating user, application, blockchain, off-chain storage, and security layers to ensure robust data handling and verification. The architecture leverages cryptographic techniques such as hashing, encryption, and digital signatures to ensure data confidentiality, integrity, and authentication. Additionally, the use of off-chain storage mechanisms addresses scalability challenges by storing large datasets externally while maintaining verifiable references on the blockchain. The workflow emphasizes the role of distributed consensus mechanisms in ensuring transaction legitimacy and preventing unauthorized modifications. By recording transaction hashes on an immutable ledger, the system enables transparent and tamper-proof data verification. The integration of blockchain with traditional architecture offers significant advantages, including decentralization, enhanced security, and improved auditability. However, challenges such as scalability, latency, and interoperability remain areas for further research. Overall, the proposed blockchain-integrated system provides a comprehensive framework for secure and transparent data transactions, making it suitable for applications across various domains, including finance, healthcare, and supply chain management.
Performance Evaluation of Contemporary Software Development Frameworks in Dynamic Environments Okouma Nguia; Ali Nguia; Joseph Kamala
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.1043

Abstract

This study presents a comparative performance evaluation of widely used frameworks, including Waterfall, Agile, DevOps, and DevOps integrated with microservices architecture. The evaluation is based on key performance indicators derived from industry-standard metrics such as deployment frequency, lead time, change failure rate, mean time to recovery (MTTR), response time, throughput, scalability, and resource utilization. The findings, illustrated through two analytical figures, demonstrate that traditional frameworks such as Waterfall exhibit limited adaptability, characterized by low deployment frequency, high lead time, and poor scalability. Agile frameworks improve flexibility and responsiveness through iterative development, yet they show moderate performance in handling dynamic workloads. In contrast, DevOps frameworks significantly enhance performance by integrating continuous integration and continuous delivery practices, resulting in improved deployment speed, reduced failure rates, and faster recovery times. The highest level of performance is observed in the DevOps combined with microservices architecture, which achieves superior results across all evaluated metrics. This is primarily due to the decentralized and modular nature of microservices, which allows independent deployment and efficient fault isolation. Overall, the study highlights the critical role of modern software development frameworks in addressing the challenges of dynamic environments. The results provide valuable insights for organizations in selecting appropriate frameworks to optimize performance and maintain competitiveness in rapidly evolving technological landscapes.
AI-Enabled Smart Energy Management Systems Using IoT for Sustainable Urban Development Fulbert Okouma; Ayim Nguia; Josepsh Gross; David Kamal; Herbert F. Bernard
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i02.1044

Abstract

The rapid growth of urban populations and increasing energy demands have intensified the need for efficient and sustainable energy management systems. Traditional energy management approaches are often inefficient, lacking real-time monitoring and adaptive control capabilities. This study proposes an AI-enabled smart energy management system integrated with Internet of Things (IoT) technologies to optimize energy consumption, improve efficiency, and support sustainable urban development. The research analyzes the contribution of various system components, including smart meters (28%), renewable energy sensors (22%), building energy sensors (20%), grid monitoring devices (18%), and demand-response systems (12%). The implementation of intelligent systems results in significant improvements in energy efficiency (34%), predictive demand accuracy (32%), system reliability (30%), cost reduction (28%), and sustainability performance (26%). The proposed framework employs a multi-layered architecture consisting of sensing, communication, processing, and application layers. Machine learning algorithms are used to analyze energy consumption patterns, forecast demand, and enable automated control mechanisms. The integration of edge and cloud computing enhances system performance by enabling real-time processing and scalable data management. The findings demonstrate that IoT and AI-driven energy management systems significantly enhance operational efficiency and sustainability. The proposed system enables proactive energy optimization, reduces waste, and supports the development of smart and sustainable cities.
IoT-Driven Smart Environmental Monitoring and Adaptive Control Systems Using Artificial Intelligence David Gross; Samson Nguia; Noumi Myong; David Karaoud
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i02.1045

Abstract

The rapid escalation of environmental challenges, including air pollution, climate change, water contamination, and noise pollution, has necessitated the development of advanced monitoring and control systems. Traditional environmental monitoring approaches, which rely on manual data collection and delayed analysis, are no longer sufficient to address dynamic and complex environmental conditions. This research presents a comprehensive framework for IoT-driven smart environmental monitoring systems integrated with artificial intelligence (AI) and adaptive control mechanisms. The study evaluates the contribution of different sensor categories in environmental monitoring systems, revealing that air quality sensors account for 32%, temperature sensors 24%, humidity sensors 18%, water quality sensors 14%, and acoustic sensors 12%. Additionally, the performance improvements achieved through intelligent systems are analyzed, showing enhancements in predictive accuracy (36%), anomaly detection (34%), response efficiency (31%), energy optimization (29%), and system adaptability (27%). The proposed framework is based on a multi-layered architecture that integrates sensing, communication, processing, and application layers. Advanced machine learning models are employed to analyze environmental data, detect anomalies, and generate predictive insights. The integration of edge and cloud computing further enhances system efficiency by reducing latency and improving real-time responsiveness. The findings demonstrate that IoT and AI-driven environmental monitoring systems significantly improve detection capabilities, data accuracy, and decision-making processes. These systems enable proactive environmental management, reduce risks, and support sustainability initiatives. The research contributes to the development of intelligent environmental infrastructures and highlights the potential of smart monitoring systems in addressing global environmental challenges.
IoT-Driven Smart Urban Infrastructure Monitoring and Predictive Maintenance Using Artificial Intelligence Md. Khalid; Jamal Uddin; Amit Kumar; Jennifer Maya
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i02.1046

Abstract

The rapid growth of urban populations and infrastructure complexity has created significant challenges in maintaining the safety, reliability, and efficiency of urban systems. This study proposes an IoT-driven smart infrastructure monitoring and predictive maintenance framework integrated with artificial intelligence (AI) and real-time analytics. The research analyzes the contribution of different monitoring components, including structural sensors (30%), traffic sensors (22%), environmental sensors (18%), energy monitoring devices (16%), and vibration/acoustic sensors (14%). The implementation of intelligent systems demonstrates notable improvements in predictive maintenance accuracy (35%), fault detection (33%), response time (31%), cost efficiency (28%), and operational reliability (27%). The proposed system utilizes a multi-layered architecture comprising sensing, communication, processing, and application layers. Machine learning algorithms are applied to analyze infrastructure data, detect anomalies, and predict potential failures. Edge and cloud computing technologies enhance system performance by enabling real-time processing and scalable data management. The findings highlight the effectiveness of IoT and AI integration in improving infrastructure monitoring and maintenance. The proposed framework supports proactive decision-making, reduces operational risks, and enhances urban sustainability. This research contributes to the development of smart city infrastructures and demonstrates the potential of intelligent systems in modern urban management.
Cloud-Based Architectural Framework for Scalable and High-Performance Smart Applications Kamal Khan; Zamil Rahman; Amrita Khan; Anamika Roy; Jhon Kabir
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.1072

Abstract

The rapid evolution of smart applications, driven by advancements in the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, has significantly increased the demand for scalable and high-performance computing infrastructures. Traditional architecture often struggles to meet these requirements due to limitations in resource scalability, processing efficiency, and system flexibility. The proposed framework emphasizes a layered architecture consisting of data acquisition, processing, service management, and application layers, ensuring efficient data flow and resource utilization. It leverages cloud-native principles to enable horizontal scalability, fault tolerance, and continuous deployment. Additionally, the integration of edge computing reduces latency by processing time-sensitive data closer to the source, thereby improving real-time responsiveness. Performance optimization techniques, including auto-scaling and load balancing, are incorporated to ensure consistent system performance under varying workloads. The framework also addresses critical challenges such as interoperability, security, and resource management by incorporating standardized interfaces and intelligent orchestration mechanisms. Experimental analysis and conceptual evaluation indicate that the proposed architecture significantly enhances system scalability, reduces latency, and improves overall application performance compared to traditional models. This study contributes to the field by providing a comprehensive architectural model that aligns with the evolving requirements of modern smart applications. The findings demonstrate that cloud-based frameworks, when combined with emerging technologies, can effectively support large-scale, high-performance systems. The proposed approach offers valuable insights for researchers and practitioners in designing next-generation smart application infrastructures.
Enhancing Transparency in Decision-Making Systems Using Explainable Artificial Intelligence Models Amit Kumar; Raisul Khan; Md. Rashid; Antu Roy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.1073

Abstract

The increasing reliance on artificial intelligence (AI) in decision-making systems has raised critical concerns regarding transparency, interpretability, and trust. Many advanced AI models, particularly deep learning techniques, operate as opaque “black-box” systems, making it difficult for users to understand how decisions are derived. This lack of explainability limits user confidence, hinders accountability, and poses ethical and regulatory challenges. This study addresses these issues by exploring the role of Explainable Artificial Intelligence (XAI) in enhancing transparency in decision-making systems. The research is conceptually supported by three key stages illustrated in the figures. First, opaque AI systems are examined, highlighting the limitations of black-box models that provide output without meaningful explanations. Second, an XAI framework is introduced, demonstrating how interpretability techniques such as feature importance analysis, rule-based reasoning, and model-agnostic explanation methods can reveal the internal logic of AI systems. These techniques enable users to understand the reasoning behind predictions, thereby improving system interpretability. Third, the study presents the outcome of integrating XAI into decision-making processes, emphasizing transparent and accountable systems that foster trust, fairness, and user engagement. A comparative methodological approach is adopted, evaluating both traditional black-box models and explainable models using interpretability and performance metrics. The findings indicate that while there may be trade-offs between accuracy and interpretability, the inclusion of XAI significantly enhances user understanding and trust in AI-driven decisions. In conclusion, this study demonstrates that explainable AI plays a vital role in transforming opaque decision-making systems into transparent and accountable frameworks. By bridging the gap between complex algorithms and human understanding, XAI supports the development of trustworthy and ethically aligned AI systems suitable for real-world applications.
Design and Implementation of Secure and Scalable Distributed Computing Systems for Modern Applications Partha Sarothi; Zulfiqur Rahman; Amrita Khan; Amit Kumar; Kamal Khan
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i02.1074

Abstract

The rapid growth of modern applications, including cloud computing, big data analytics, and Internet of Things (IoT), has significantly increased the demand for secure and scalable distributed computing systems. Traditional centralized architecture is no longer sufficient to handle large-scale data processing and dynamic workloads, leading to the adoption of distributed computing paradigms. This study presents the design and implementation of a secure and scalable distributed computing framework, supported by performance evaluation through analytical figures illustrating system scalability, resource utilization, latency, and security effectiveness. The analysis demonstrates that distributed architectures significantly improve system scalability by enabling horizontal scaling and efficient workload distribution across multiple nodes. The figures highlight that as the number of nodes increases; system throughput improves while latency is reduced through optimized communication and load balancing mechanisms. Additionally, the implementation of advanced security protocols, including encryption, authentication, and access control, enhances system resilience against cyber threats. The results further indicate that the integration of containerization and orchestration technologies, such as Kubernetes, improves resource utilization and system reliability. Security evaluation metrics show a reduction in vulnerability exposure and improved threat detection capabilities in distributed environments. However, the figures also reveal challenges related to network latency and resource management, particularly in highly dynamic environments.

Page 12 of 13 | Total Record : 122

 9   10   11   12   13 

Filter by Year

2023 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 03 (2026): The Eastasouth Journal of Information System and Computer Science (ESISCS) Vol. 3 No. 02 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS) Vol. 3 No. 01 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS) Vol. 2 No. 03 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS) Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS) Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS) Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS) Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS) Vol. 1 No. 01 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)