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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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AEGIS-FL: Auditable Federated Threat Detection for Multi-Tenant Cloud-Edge Systems Kesavan Sundara Mudaliyar; S. Satish Kumar
The Eastasouth Journal of Information System and Computer Science Vol. 4 No. 01 (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.v4i01.1217

Multi-tenant hybrid cloud and edge infrastructures generate security telemetry that is individually sparse and collectively informative, but contractual, regulatory, and competitive barriers prevent tenants from pooling it. Federated learning offers a route around this obstacle, yet deployments in adversarial security settings must simultaneously resist model poisoning by participating tenants, provide auditable evidence of what each tenant contributed, bound the information leaked about tenant-local data, and translate detections into containment actions. Existing work addresses these requirements in isolation. We present AEGIS-FL, a four-plane architecture that couples DataOps-oriented governance, privacy-preserving federated training, a permissioned audit ledger, and a reinforcement-learned response controller, and we evaluate the planes jointly rather than separately. The central mechanism is a ledger-anchored aggregation rule in which per-tenant reputation, persisted across rounds on the audit ledger, sets an adaptive screening budget for a geometric (multi-Krum) filter. This removes the dependence on oracle knowledge of the adversary fraction that, we show, silently inflates reported robustness in the standard experimental protocol. On a controlled 100-tenant simulation with label-skewed partitions, AEGIS-FL reaches F₁ = 0.811 ± 0.017 against 0.790 ± 0.048 for FedAvg and 0.259 ± 0.005 for isolated per-tenant training and sustains F₁ = 0.799 under 10% sign-flipping adversaries where FedAvg falls to 0.740. Secure aggregation reconstructs the plaintext mean exactly under 30% tenant dropout at 19.2 kB per tenant per round, and the audit ledger commits at 19.6 kB per round with seven-hash Merkle inclusion proofs. We report three cautionary findings that qualify the deployment envelope. First, client-level differential privacy at this federation scale cannot reach a meaningful budget: ε ≈ 103 costs 4.0 F₁ points, while ε ≈ 7 destroys utility (F₁ = 0.308). Second, contrary to our own hypothesis, reducing model dimensionality does not recover private utility, because capacity losses offset noise reduction. Third, a loss-threshold membership-inference attack achieves AUC = 0.502 even without noise, so the empirical privacy benefit of the noise mechanism is not demonstrable in this regime. We argue these results indicate that participant scale, not noise calibration or model compression, is the binding constraint on private federated security analytics.

Data-Driven Predictive Modeling for Early Diagnosis and Risk Stratification of Chronic Diseases Md Jubayar Hossain; Mohammed Majid Bakhsh; Muhammad Adnan; Evha Rozario; Sraboni Clara Mohonta; Tushar Roy; Md Imtiaz Faruk; Emran Hossain
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.v2i01.1220

Chronic kidney disease (CKD) often remains undetected until advanced stages, limiting opportunities for timely intervention and increasing the burden of complications and treatment. This study proposes a data-driven hybrid ResNet50-SVM framework for the early diagnosis and risk stratification of CKD. Clinical data were prepared through missing-value treatment, data cleaning, Min-Max normalization, reduction of redundant information, and optimized feature selection. The processed dataset was divided into training and testing subsets using an 80:20 ratio. ResNet50 was employed to extract high-level representations, while a support vector machine performed the final classification. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion-matrix analysis, and training and validation behavior. The proposed framework achieved an accuracy of 98.76%, precision of 97.65%, recall of 99.21%, and F1-score of 98.34%. It also outperformed logistic regression, a convolutional neural network, and a decision tree across the reported evaluation metrics. The high recall indicates a strong capacity to identify patients with disease while reducing missed positive cases. These findings demonstrate the potential of hybrid deep-learning and machine-learning methods to support accurate, scalable, and timely CKD screening. Further validation using larger, more diverse clinical datasets and explainable AI methods is needed before deployment in real-world clinical decision-support systems.

Contract-Drift Detection in Event-Driven Microservices Using Runtime Message Semantics: A Literature Synthesis Srinivas Nune
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.v1i01.1221

Event-driven microservice architectures largely dispense with synchronous interfaces, in which producer and consumer services are bound to a shared interface at compile time, and rely instead on asynchronous message contracts that may evolve between services without explicit notice — a condition termed contract drift. This paper synthesises fourteen peer-reviewed and archival papers published between 2017 and 2022 across five domains: microservice architecture, consumer-driven contract testing, semantic versioning, runtime-verification theory, and distributed observability. The synthesis finds that prevailing assurance mechanisms operate predominantly offline and pre-deployment, and therefore cannot detect drift that emerges once a service is in production. Building on an established runtime-verification taxonomy, the paper proposes a structural-similarity measure and a type-agreement measure for quantifying the extent of contract drift; both are computed in flight against a registered baseline contract, and an alert is raised when their weighted combination exceeds a configurable threshold. The available evidence indicates pronounced recent interest in the problem — six of the fourteen papers synthesised were published in 2022 — and suggests that a decentralised, broker-side monitoring architecture is a viable design approach. Open challenges remain in baseline governance, behavioural drift detection, and the absence of standardised benchmarks for comparing runtime contract-monitoring techniques.

Context-Aware Document Intelligence for Automated Compliance Evidence Extraction from Heterogeneous Financial Documents Varsha Shah
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.v1i01.1222

Regulatory compliance activities across the financial sector produce a vast and growing amount of unstructured evidentiary material, such as loan agreements, non-disclosure agreements, annual reports, disclosure forms, scanned correspondence, and more — all of which auditors and regulators must review to assure compliance with disclosure, anti-money-laundering and data-protection requirements. Text-only Natural Language Processing pipelines are unable to handle this type of content because the facts relevant to compliance are often embedded in the format of a page, such as signature blocks, tabular disclosures and heading to clauses. This paper summarizes the results of two and a half years of work, including twenty-five papers from the field of transformer language modeling, the field of layout-aware document representation learning, the field of financial natural language processing and the field of applied regulatory-technology systems, all of which were published between 2015 and 2022. It explores how the performance of text-only encoders jumped from an F1 score of around sixty percent on benchmarks for form understanding to the layout-aware multimodal architectures that exceeded the ninety percent mark on the same benchmark, and reviews applied systems that lowered the manual anti-money-laundering review workload by more than fifty percent while maintaining detection rates above eighty percent. The review also outlines five benchmark datasets that test extraction tasks that are related to compliance and suggests a generalized six-stage extraction pipeline extracted from the reviewed architectures. The results show that layout-conditioned pretraining yields the highest improvements, not just a parameter scale, and that the human verification is a constant characteristic of all the deployed systems studied.

Semantic Idempotency for Exactly-Once Business Effects in At-Least-Once Distributed Messaging Systems Karthik Juluri; Srinivas Nune
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.v1i01.1223

At-least-once delivery is the dominant message-delivery paradigm in distributed messaging infrastructures, because stronger transport-level guarantees require blocking coordination that compromises availability and throughput. This paper gathers, from the foundational and modern literature, the concepts of message delivery semantics, of idempotency theory and of distributed coordination patterns, articulating a conceptual framework, called semantic idempotency, where exactly-once business effects are achieved on top of an at-least-once transport, by relying on deduplication performed on the receiver side instead of on the transmitter side. It starts by tracing the history of the various studies that led to the development of logical clocks and consensus impossibility theorems, quorum-based replication, log-structured messaging, snapshot algorithms, saga-based compensation, and conflict-free replicated data types, and how each addresses a partial solution to the duplicate-delivery problem. The analysis reveals that redelivery probability increases non-linearly with the number of retries at realistic acknowledgment-loss rates, the idempotency-key registry adds approximately one millisecond of median latency compared with mid-single-digit milliseconds for two-phase commit, and windowed key-store retention can maintain over 95 percent accuracy of duplicate-catching while keeping state growth under control. Idempotent receivers preserve the availability and horizontal scalability of at-least-once transport, at the cost of weaker native consistency guarantees than two-phase commit provides. The paper also presents the formalization of semantic idempotency using an operator-oriented notation that separates redelivery at the transport level from application of effects at the business level. These results suggest that architects should regard idempotency as a first-class contract between product and consumer, while the future of messaging systems might be a platform that includes a deduplication registry as a first-class citizen rather than an ad hoc application programming feature.

Failure-to-Risk: Predicting Compliance Breaches from Process Anomalies Using Temporal Explainable AI Varsha Shah
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (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.v1i03.1224

Non-conformances in organizational processes are often observed as slight irregularities in the sequence of events, prior to even the formal event of a non-conformance being reported, leading to an increased interest in predictive models that can detect such irregularities and give a reason for each alert. This review summarizes the methodological progress of predictive business process monitoring, temporal deep learning and explainable AI and proposes an overall conceptual framework, called temporal explainable AI for compliance-risk prediction, which brings together sequence encoders (e.g. LSTM networks, attention-based transformers) and explainers for post-hoc interpretation (e.g. Shapley additive explanations, local surrogate models, rule-based anchors). The synthesis moves from classical models of stochastic processes to data-driven temporal architectures, laying out the corresponding loss of native interpretability for predictive flexibility, and describing how post-hoc explanation methods have been devised to reclaim the lost interpretability. The results of a comparative review of benchmark evaluations on predictive process monitoring also show that key aspects of the evaluations that are relevant to predictive process monitoring, such as the encoding strategy, cross-log generalization, or handling of class imbalance, have a significant impact on the reported performance and should be taken into account when designing a deployed compliance-risk system. A comprehensive temporal explainable AI system for compliance monitoring should also include concept-drift detection, as the statistical correlation between process behavior and compliance outcomes will not likely be constant throughout an organization's lifecycle.

Schema-Evolution-Safe Polyglot Persistence for Containerized Microservices Using Event-Carried State Transfer A Literature Synthesis Srinivas Nune
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (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.v1i03.1225

Polyglot persistence is the new approach for container-orchestrated microservices, where each microservice chooses a data-store technology that best fits its workload; and event-carried state transfer, where state changes are carried as immutable events through a shared log, instead of directly through synchronous calls between services. This literature survey investigates the interplay of three research lines: schema evolution in heterogeneous NoSQL and polyglot data stores, event-driven integration patterns to minimize synchronous coupling and distributed transaction management by the saga pattern. The review is based on the distributed-systems theory, empirical microservice-migration studies, and schema-evolution measurement studies, to synthesize a conceptual framework that enables safe, independently evolvable microservice data layers through schema-registry-governed compatibility checking, event-carried state transfer, and compensation-based transaction management. The synthesis emphasizes the classic dilemma of consistency vs availability, as summarized by the CAP theorem, as well as the operational rigidity of production database schemas, and the flexibility that NoSQL stores with schema-on-read are offered but not guaranteed. Four conceptual diagrams and three comparative tables are used to summarize the reviewed material as an integrated architectural viewpoint, whereas two formal notations are used to summarize the consistency and transaction-compensation reasons that underlie the framework. The review finds that schema governance, compatibility contracts and compensating transactions are complementary mechanisms that do not compete and finds open challenges around tooling maturity, consistency on cross-store data and schema-level lifecycle management at the container-orchestration level.

Semantic Customer 360: A Context-Preserving Architecture for Unifying Heterogeneous Enterprise Customer Data Swapna Putti
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (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.v1i03.1226

Customer information is handled in enterprise organisations through customer relationship management systems, transactional databases, support systems, and marketing systems, where the meaning is coded using incompatible local schemas. Without capturing the context of the source of those records, where and when they were created, and which regulatory contexts the records are relevant to, such unified views of customers are complete in content but lacking in interpretive fidelity. This paper aims to compile the literature in the fields of context awareness, linked data, entity resolution, ontology-based data access, master data management, knowledge graphs, data lake architecture and data protection regulation to propose an architecture for the Semantic Customer 360 that aims to preserve explicit contexts, and not merely data consolidation. The architecture places a governed data-lake ingestion zone, a hybrid schema-matching module, a probabilistic and graph-based entity-resolution pipeline, and a knowledge-graph-structured semantic layer under the hood of customer-facing analytics and service consumption. The synthesis shows that several candidate comparison techniques can lead to over 10 times fewer comparisons than naive pairwise comparison, that a choice of master data management strategy has measurable impact on governance overhead, that ontology-based mediation can also enable access for queries to sensitive data without moving or copying physical data, and that perceived regulatory compliance is measurable and correlated with customers' trust. The architecture proposed along with comparative analysis it provides can help enterprise data architects to gauge unstructured approaches to evaluating matching algorithms, integration strategies and governance mechanisms that can help them harmonize disparate customer data while keeping them easily traceable and audit-friendly in the present landscape of data protection requirements.

AI/ML and Generative AI Use Cases in Financial Services Ravikumar Mani Naidu Gunasekaran
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.v2i02.1227

Artificial Intelligence (AI), Machine Learning (ML), and Generative AI are transforming the financial services industry by enabling smarter decision-making, improved operational efficiency, and enhanced customer experiences. Financial institutions increasingly leverage these technologies across a wide spectrum of use cases, including fraud detection, risk management, regulatory compliance, customer personalization, algorithmic trading, and intelligent automation. ML models analyze vast volumes of structured and unstructured data to uncover patterns, predict risks, and optimize processes, while Generative AI extends these capabilities by synthesizing insights, automating content creation, and enabling conversational intelligence. In banking and capital markets, AI-driven solutions support credit scoring, anti-money laundering (AML) monitoring, and predictive analytics for market trends. Insurance firms use AI/ML for underwriting, claims automation, and fraud prevention, while wealth management platforms integrate AI-powered advisory services to deliver personalized investment strategies. Generative AI further enhances productivity through automated report generation, document summarization, and intelligent virtual assistants, reducing manual intervention in compliance and customer servicing workflows. Despite significant benefits, challenges remain in ensuring data privacy, model explainability, regulatory compliance, and governance. Financial institutions must adopt responsible AI frameworks to mitigate risks associated with bias, security, and transparency. Overall, the integration of AI/ML and Generative AI is reshaping financial ecosystems, enabling innovation, improving resilience, and driving competitive advantage in an increasingly digital and data-driven landscape. This paper explores the transformative impact of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) on the financial services industry. It highlights real-world applications, benefits, challenges, and regulatory considerations, offering a roadmap for institutions aiming to harness these technologies for strategic advantage

NFC-Enabled Tap-to-Activate: A Secure, Event-Driven Architecture for Instant Card Activation in Global Digital Banking Platforms Anil Mandloi
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.v2i02.1228

Card activation remains a persistent source of friction in digital banking. Traditional methods that rely on call centers or web forms introduce multi-minute delays, elevate operational cost, and create openings for social-engineering attacks. This paper presents a practical architecture that allows cardholders to activate a newly issued plastic card by simply tapping it against an NFC-enabled smartphone running the issuer’s mobile application. Physical possession is proven through a short-range NFC exchange that yields a dynamic EMV-style cryptogram. The resulting event is published to an Apache Kafka event backbone and processed by loosely coupled microservices responsible for risk evaluation, host validation, status update, and multi-region synchronization. Security is treated as a cross-cutting concern that encompasses tokenization, cryptogram validation, and continuous authorization consistent with zero-trust principles. Using publicly available contactless adoption statistics from 2018–2022 and latency figures reported for Kafka-based banking pipelines, the architecture is shown capable of completing activation in a few seconds under normal network conditions. Tables and figures quantify the expected improvement relative to traditional channels and document the growth of the contactless card base that makes the approach feasible at scale.

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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

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Kota adm. jakarta barat,

Dki jakarta

INDONESIA