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The Eastasouth Journal of Information System and Computer Science

esiscs
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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 198 Documents

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Business Analytics for United States Retail Channel Planning Under Structural Change Md Jubayar Hossain; Semanto Saha Utsab; Sadia Afrin Jarir; Evha Rozario; Zerin Akter Tanni; Tushar Roy
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): 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.v3i01.1263

Retail channel planning requires forecasts that remain useful when purchasing patterns change abruptly. This study examines the quarterly e-commerce share of United States retail sales using 99 observations from 2000 Q1 through 2024 Q3 in the data vintage available on December 31, 2024. Census Bureau estimates distributed through ALFRED provide the sole empirical data source. Six transparent specifications are compared at 59 successive one-quarter forecast origins using chronological estimation. Evaluation distinguishes 2010–2019, 2020–2021, and 2022–2024 Q3 and combines absolute error, squared error, directional bias, and asymmetric planning-loss sensitivity. The lowest mean absolute errors in these periods are 0.0755, 0.9375, and 0.1215 percentage points, respectively. Method rankings depend on the evaluation period and criterion. The findings support period-specific assessment, simple benchmarks, and explicit attention to planning consequences. Results use a fixed historical data vintage; they do not establish causal pandemic effects or realized firm-level savings.

Cost Sensitive Machine Learning for Credit Risk Assessment and Lending Decisions Muhammad Imran Syed; Mitu Akter; Semanto Saha Utsab; Md Tanvirul Islam; Yeasmin Jahan
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): 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.v3i01.1264

Machine learning can improve credit risk prediction, yet predictive gains do not automatically translate into better lending decisions. The financial value of a score depends on which errors it makes, whether its probability is calibrated, and how its operating threshold reflects the costs of approval and rejection. This study develops an analytical framework linking these quantities for statistical and ensemble credit scoring models. Published findings on classifier benchmarking, imbalanced credit data, behavioral information, cost-sensitive learning, and distributional effects establish the research context. The original analysis derives feasible loss bounds from accuracy and class prevalence, identifies conditions for economic dominance between classifiers, and examines sensitivity to error costs and probability uncertainty. Deterministic scenarios use adverse class prevalences from 5% to 50%, relative error penalties from one to ten, and classifier accuracies from 70% to 95%. No borrower-level models are newly trained. At 85% accuracy, 30% adverse prevalence, and a five-to-one error penalty, feasible loss ranges from 150 to 750 units per 1,000 applications. Two feasible classifiers with accuracies of 85% and 80% can incur losses of 750 and 200 units, respectively, demonstrating a possible reversal between statistical and economic rankings. Expected loss minimization gives a probability threshold of one sixth at a five-to-one penalty ratio. A general regret expression also shows why probability uncertainty near that threshold deserves particular attention. The framework explains how findings from credit scoring research can inform model selection without treating results from different populations as directly comparable. It supports evaluating the combined prediction and decision process through discrimination, calibration, financial consequences, and distributional outcomes.

Risk-Aware Federated Cyber Defense for Cloud–Edge Systems Through Privacy, Network Resilience, and Accountable Response Celeste Yvonne Ramirez
The Eastasouth Journal of Information System and Computer Science Vol. 4 No. 02 (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.v4i02.1267

Cloud services, edge devices, and organizational information systems expose security teams to a shared problem: a locally accurate detector can still produce a poor operational decision when its recommendations ignore service dependencies, tenant boundaries, privacy constraints, or the consequences of interruption. This paper develops a design-science framework for coordinating these concerns within a single decision process. A purposive corpus of 55 supplied publications informs the requirements; it is treated as a bounded design resource rather than a systematic or exhaustive evidence base. The proposed architecture connects tenant-local telemetry, context-sensitive threat estimation, privacy-preserving collaborative learning, dependency-aware response selection, and verifiable governance records. Its central contribution is a constrained decision formulation that minimizes expected attack loss and response-induced disruption while enforcing explicit limits on privacy expenditure, latency, and service availability. Analytical examination establishes three conditional properties: independent event thresholds can violate shared service constraints, selective abstention can reduce decision loss when review is sufficiently informative, and federated participation does not by itself establish privacy. A transparent worked example demonstrates how a capacity constraint changes which alerts receive an automated response; the example is illustrative and does not represent measured performance. A prospective evaluation protocol specifies chronological and tenant-separated testing, rare-attack assessment, poisoning experiments, privacy accounting, recovery analysis, and paired comparisons against operational baselines. No experiments or production deployments are reported. The framework provides an implementable research specification and testable propositions for examining whether coordinated cyber defense improves operational outcomes beyond predictive accuracy alone.

Semantic Contract Meshes for Dynamic Partner Orchestration Across Multi-Enterprise Cloud Ecosystems Shivakrishna Vangala
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 01 (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.v1i01.1268

Multi-enterprise cloud ecosystems need a coordinated and accelerated approach to onboard, coordinate, and replace partners, that conventional bilateral integration agreements cannot keep up with. This systematic literature review aims to describe and assess the semantic contract mesh, a decentralized layer that is characterized by the binding of machine-interpretable agreements to shared ontologies, enforcement through zero-trust policy points, and execution through verifiable workflow models, and that has been published from 1990 to 2021. Sources were rated on six weighted criteria and put into 7 themes with five having at least 4 sources. Three latency regimes are suggested along with a formal contract tuple, a weighted Jaccard compatibility measure and a Petri net state equation. Comparative scoring gives the mesh a weighted 2.75 out of 3.00 points while point-to-point integration gets a 0.65. With an illustrative parameterization, mesh latency is 38.0ms at 50 partners, while pairwise integration takes 1,225ms. Latencies and scores are analytic, not experimental. In just 8 of the 42 theme–capability cells, the maximum code is achieved, revealing fragmentation across the literature. Ethical, regulatory and scalability challenges are explored, and a phased, ontology-first approach to adoption for enterprises seeking to incrementally move from bilateral integration to shared machine-interpretable agreements is proposed.

Architectural Decomposition Strategies for Migrating Legacy Java EE Monoliths to Cloud‑Native Microservices Tathagata 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.v1i02.1269

This paper presents a structured technical roadmap for decomposing legacy Java EE monoliths into independently deployable, cloud native microservices. The modernization effort focuses on enterprise applications built on JSPs, Servlets, Struts 2.0, EJBs, MDBs, Axis based SOAP services, and iBATIS driven persistence layers. These systems typically rely on tightly coupled modules, shared state, and synchronous communication patterns that limit scalability and operational resilience. The proposed approach outlines the architectural refactoring required to migrate these components into a Kubernetes containerized environment. Objectives include the replacement of point to point and JTA-based two-phase commit workflows, the transformation of MDB based asynchronous logic into Kafka consumer services, the evolution of Axis SOAP endpoints into RESTful interfaces, and the transition from shared schema iBATIS implementations to decentralized, database-per-service designs. The resulting framework provides a repeatable methodology for reducing architectural debt, improving throughput, and supporting compliant processing in regulated domains such as financial services.

Event-Safe Agent-to-Cloud Fabrics for Transactionally Consistent Autonomous Enterprise Systems Shivakrishna Vangala
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 02 (2025): 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.v3i02.1270

The more and more autonomous software agents perform business operations against the shared cloud state, the more duplicated, misordered and irrecoverable effects are experienced by enterprises in the event of failure and retry. A systematic review of 26 peer-reviewed sources over 46 years was carried out to see how transactional consistency can be ensured from the agent to the cloud. A rubric for eight coordination mechanisms was developed with six criteria, and sources were coded into five domains. The synthesis demonstrates that crash tolerance is 2f + 1 replicas, Byzantine tolerance is 3f + 1, and that update-anywhere replication is N^3 times as many conflicts, where N is the number of nodes, or that deterministic sequencing has been reported at about 500,000 transactions per second. A composite event-safe fabric, by combining a durable intent log, per domain sequencing, idempotent effects and sagas, as well as convergent agent memory, achieved a composite score of 2.45, compared to a mean of 1.94 for single mechanisms. The ranking assumes a certain level of weight for the effect safety, and the conclusions are based on ordinal coding instead of measured agent workloads. Architectural guidelines for designing agent enabled enterprise platforms and directions for empirical research are provided.

Observability for Java Microservices: Metrics, Tracing, and Logging in Cloud‑Native Systems Tathagata 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.v1i02.1285

Distributed Java microservices require comprehensive observability to maintain reliability, diagnose performance degradation, and support operational continuity across diverse cloud native and hybrid environments. This paper presents a unified framework for monitoring Spring Boot based microservices through metrics instrumentation, distributed tracing, structured log aggregation, and infrastructure level telemetry. The study identifies essential JVM, application, and platform indicators required for effective performance analysis, including garbage collection behavior, thread pool utilization, connection pool saturation, request latency, and error propagation patterns. Techniques for instrumenting Spring Boot applications using Micrometer, OpenTelemetry, and structured logging are examined, along with strategies for exporting telemetry to platforms such as Dynatrace, Prometheus, Datadog, New Relic, Splunk, and Elastic Stack. The framework extends to asynchronous processing pipelines by outlining critical Kafka producer, consumer, broker, and topic level metrics such as consumer lag, partition health, request latency, and under replicated partitions that enable early detection of backpressure and message flow disruptions. Infrastructure level signals, including container resource limits, node pressure, CPU throttling, pod restarts, and autoscaling activity, are incorporated to support SRE driven diagnostics and correlation. The paper defines recommended dashboards, alert thresholds, and telemetry correlation models that reduce mean time to resolution and provide deep visibility into Java microservices operating at scale.

Performance Optimization for Spring Boot Microservices: Adaptive Memory, Concurrency, and Resource Management Tathagata Roy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 01 (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.v1i01.1286

Containerized Spring Boot microservices frequently encounter performance limitations arising from inefficient JVM memory allocation, blocking concurrency models, and suboptimal resource configuration. This paper presents a comprehensive analysis of optimization strategies for Java‑based microservices running on Spring Boot and Java 17, emphasizing adaptive memory management, thread‑pool tuning, and reactive resource handling. The study examines dynamic heap‑sizing techniques that align JVM memory parameters with container constraints to reduce garbage‑collection frequency and prevent allocation failures. Off‑heap memory approaches, including Netty and native allocation, are evaluated for their ability to lower heap pressure in high‑throughput I/O workloads. Concurrency optimization is addressed through queuing‑theory‑based sizing of fixed and cached thread pools to minimize contention and improve throughput in traditional blocking execution models. The analysis extends to data‑access layers, where reactive database connectivity (R2DBC) and dynamic connection‑pool tuning algorithms are applied to reduce thread waiting time and tail latency. Additional improvements include JPA query and pooling optimizations, Tomcat connector configuration, Spring Boot tuning practices, and Docker image refinements that reduce startup time and memory footprint. The combined evaluation provides a unified framework for enhancing responsiveness, scalability, and resource efficiency in cloud‑native Spring Boot microservices.

Risk-Bounded Authorization and Runtime Verification for AI Agents Executing Sensitive Financial Operations Anil Mandloi
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): 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.v3i01.1287

AI agents are increasingly used to initiate or approve sensitive financial operations such as transfers, settlements, and limit changes. Conventional access-control models that rely on static roles or attributes do not adequately constrain the cumulative risk of agent-driven actions. This paper presents a design that combines a quantitative risk-bounded authorization decision with continuous runtime verification of operational invariants. Authorization is granted only when the computed risk of the requested operation lies inside a policy-defined bound; subsequent execution is monitored against temporal and safety properties, with automatic blocking or compensation on violation. Evaluation on a synthetic workload of 40,000 agent-initiated operations shows lower policy-violation rates than a static ACL baseline and a risk-score-only approach, while authorization latency remains acceptable under moderate concurrency. The approach is intended as a practical control layer rather than a complete replacement for human oversight in regulated environments.

The Role of SAP Business Technology Platform in the Intelligent Enterprise: Developing and Applying an Intelligent Enterprise Capability Framework Abhinav Jain
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.v1i02.1288

The rapid digitalization of enterprises has increased organizational dependence on cloud applications, distributed data, application programming interfaces, automation, and artificial intelligence. The COVID-19 pandemic accelerated this transition but also intensified a persistent architectural problem: digital capabilities frequently become fragmented when introduced as independent technologies. The challenge facing enterprises today is therefore shifting from digital adoption toward the integration and coordination of digital capabilities. This study examines SAP Business Technology Platform (SAP BTP) as an architectural foundation for the Intelligent Enterprise. Drawing on research in digital transformation, enterprise architecture, and digital-platform ecosystems, the paper proposes the Intelligent Enterprise Capability Framework (IECF). The framework evaluates five enterprise capabilities: Connectivity, Adaptability, Context, Execution, and Intelligence. To make the framework evaluative rather than purely descriptive, each capability is assessed using three predefined criteria: implementation maturity, breadth of deployment, and strength of documented outcome evidence. The framework is applied to three evidence sets: an SAP-sponsored IDC study of nine SAP BTP customers, Maersk's documented BTP implementation, and Hitachi High-Tech's side-by-side extensibility program. The resulting overall IECF scores are 3.93 for the IDC sample, 3.67 for Maersk, and 3.73 for Hitachi High-Tech on a five-point scale. All three exceed the study's exploratory threshold of 3.5 for meaningful demonstrated platform value, producing an aggregate score of 3.78. The analysis further shows that platform value is realized through different capability profiles: IDC demonstrates stronger Context and Execution outcomes, Maersk demonstrates strong cross-system Connectivity and capability coordination, while Hitachi High-Tech demonstrates particularly strong Adaptability and ERP-core decoupling. The study contributes a capability-centric method for evaluating enterprise technology platforms and argues that an Intelligent Enterprise should be understood not as the implementation of an individual technology but as an architectural capability created when systems, data, applications, automation, and intelligence operate together.

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Contact Name Abdul Aziz

Contact Email abdulazizbinceceng@gmail.com

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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

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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