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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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Interface-Centric Leadership Framework for ADAS–Restraint Applications Engineering and Vehicle Integration Chethan Kanakuppe Siddegowda
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.v1i01.1305

The decisions that a vehicle makes regarding braking in case of a crash are now determined by advanced driver assistance functions, which are being fed by restraint systems. This connection fosters interfaces which are not owned by any single functionality team. Preexisting literature characterises sensors, functional safety, scenario testing, but these are discussed separately, task structure and team coordination. The paper suggests an interface centric leadership framework to ADAS restraint applications engineering and vehicle integration. There are four layers within the framework and they are interface identification, interface ownership, interface verification and interface governance. Two scoring functions rank interfaces by criticality and the ability of the leaders to cover the critical set. A timing margin equation is used to connect the decision of the leaders with the pre-crash window that is provided to the restraint controller. The experiment conducted by the study evaluated the framework using a program test with 42 interfaces in three releases of the vehicle program. The experiment was between a baseline team which was functional headed and a team which was interface headed. The interface led team discovered 71 percent of its integration defects prior to vehicle level test, as compared to 38 percent of the baseline. The number of late interface changes decreased from 19 to 6. The average time interval closes an interface defect dropped to 8.1 days as compared to 14.2 days. Pre-crash timing margin increased to 63 ms as compared to 41 ms. Findings indicate that leadership associated with interfaces enhances the quality of integrations as well as restraint preparedness. The model provides engineering managers with a direct approach to allocating responsibility and safeguarding schedule differences.

Lifecycle Assurance Leadership for Multi-Plant ADAS and Restraint Product Deployment Chethan Kanakuppe Siddegowda
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (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.v2i03.1306

A number of plants are now simultaneously associated with the manufacture of advanced driver assistance module and airbag restraint controllers by auto parts suppliers. Each plant introduces a different variation in the calibration, testing, supplier management, and change management. To address this issue, this paper suggests the lifecycle assurance leadership as a managerial model, which links safety decisions integral to all stages up to concept, and field feedback. The model has six-dimension Lifecycle Assurance Index and a central assurance office where plant level leads are considered. We experimented the model in a year-long program in five plants which produced forward camera modules, front radar units and restraint controllers. The defect rate in the network decreased from 57.4 percent to 62.0 parts per million. Calibration escapes reduced by 71 percent and the time of audit findings reducing as much as from 41 to 17 days. There was a reduction of defect rate spread in plants by 38 percent. Regression analysis indicates that every one-unit increment in the index decreased the defects by 2.9 parts per million. Ramp up learning went up by a factor from 0.18 to 0.31. These findings show that leadership practice can be used to identify most of the quality difference between the plants.

Trust-Calibrated Human-AI Collaboration for Engineering Design Review and Program Governance: A Systematic Review Yogesh Gawade
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.v2i01.1307

Artificial Intelligence (AI) is now viewed as an analyst and decision support in the field of engineering design review and program governance, but the benefit of using AI as such will be contingent upon the level of human dependency to gain from system reliability. This systematic review is a synthesis of 28 sources published from 1997–2023 about human-AI interaction, trust in automation, explainable AI, AI ethics, and algorithmic auditing, and maps these sources to the stages of systematic engineering design. Each source was assigned to one of the constructs of trust, reliability outcomes, types of interventions, and governance functions. From the synthesis, reliability, transparency, and feedback are the main drivers of trust in AI, having been identified as an appropriate design target by over 18 interaction guidelines distilled from well over 150 recommendations, a review of 127 trust in automation studies, and a review of approximately 150 empirical trust in AI studies. While explanations led to higher acceptance of correct and incorrect recommendations, cognitive forcing and confidence displays resulted in better calibration at the expense of usability. Transparency was mentioned in 73 of 84 ethics guidelines and a national risk management framework was recently established, in which governance has four functions. A trust-calibrated review architecture is suggested that combines the level of automation per phase, seven formalized metrics and auditable governance gates, providing measurable criteria for implementing AI in review processes with safety and cost implications.

A FinTech-Oriented Data Mesh and Lakehouse Architecture for Governed, Real-Time Financial Analytics Gopinathan Rathinavelu
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.v1i01.1308

Financial institutions depend on timely, trustworthy, and traceable data to support fraud detection, anti-money-laundering (AML) monitoring, credit-risk assessment, payment operations, customer analytics, and regulatory reporting. Centralized data platforms can become delivery bottlenecks when business domains use different systems, definitions, access models, and release cycles. This paper proposes a FinTech-oriented enterprise data platform that combines Data Mesh principles with a Lakehouse foundation, and describes it through three complementary views: a platform overview that separates data creators from data consumers, a logical architecture in which domain-owned storage and compute are exposed as one logical lakehouse under governance, operational, and development planes, and an operating model in which domain, platform, and governance teams publish bronze, silver, and gold data layers. Domains such as payments, cards, customer identity, lending, fraud, and compliance publish governed data products through standard contracts. A shared platform provides batch and streaming ingestion, open table storage, virtualization and federated querying, metadata discovery, policy-based access, lineage, quality checks, and automated deployment. Federated computational governance sets common controls while domain teams manage products close to their business context. The paper presents requirements, components, representative financial workflows, governance controls, and an evaluation plan. It reports a reference design, not a measured deployment; expected benefits are therefore stated as hypotheses to be tested in a pilot.

Event-Driven Decomposition Strategies for Migrating Monolithic Enterprise Platforms to Resilient Microservice Architectures Shubh Prabhat
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.v1i02.1309

Enterprise platforms are often operated in large monoliths. They are moved to microservices by teams to achieve scale, quicker releases and isolation of failures. Most of these moves do not provide resilience since services make long synchronous chains among themselves. The paper suggests an event-based decomposition plan to such migrations. There are five phases of the strategy. They consist of domain mapping, cut scoring, event contract design, strangler extraction and fault injection testing. The work is directed by two scores. A cut score is measured to assess the effectiveness of a service in retaining its calls and data within the boundary of the service. An event conversion ratio calculates the number of cross service interactions that consume events as compared to direct call. Design decisions are related to resilience through a path availability equation and an event lag equation. The test is carried out on a virtual order management system with 1.4 million code lines and 236 database tables. The site is divided into two services into eight parts. One of them is synchronous calls. The remaining uses the suggested strategy. The foundation of a monolith is the basis. The event driven version supports 1,000 orders/minute with a p95 latency of 360 milliseconds compared to 655 milliseconds in the synchronous version. The one service outage completes 91 percent against 47 percent of orders. The recovery time decreases from 22 to 9 minutes. The variations also decrease from 3.9 to 1.6 per 10,000 orders. After release, one of eight service boundaries had to change. The event driven version takes 38 person months compared to 31. The cuts should be influenced by event design.

Graph-Based Dependency Intelligence for Predictive Failure Analysis in Large-Scale Distributed Systems Shubh Prabhat
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.v2i02.1310

Distributed systems with many services fail via the distributed processes of dependent services, and most monitoring systems do not respond to problems until the first symptom occur. This article introduces a graph-based dependency intelligence architecture, which is used to predict failures that are going to impact users. The framework constructs a weighted service dependency graph using traces, metrics and logs, and trains the graph in each time window, and uses a graph neural network to make a prediction about the likelihood of each service failing within the next fifteen minutes. The edge weight is defined by three equations, the message passing rule and the probability of failure and a fourth equation of the evaluation score. The paper provides a survey of the literature on microservice faults, root cause localization and graph learning, and a comparison of six recent diagnosis studies with the prediction gap. A benchmark of 41 services and 126 dependency edges on a cluster of 12 nodes was utilised in the experiment. The team infused 300 failure instances between four classes over 30 days and evaluated the models on 120 unknown instances. The proposed model achieved precision that was equal to 0.89, a recall of 0.87 and an F1 score that was 0.88. A model that was a static graph model achieved 0.79, service level LSTM reached 0.69 and threshold alerting reached 0.56. The suggested model provided a mean warning lead time of 8.7 minutes, reduced the false alarms to 2.1 per day and the true root service was ranked in top three ranked services in 81 percent of episodes. Findings indicate that the dynamic dependency weights provide predictive power over and above the service level signals.

A Data-Driven Explainable AI Framework for Predicting Industrial Equipment Failures Using Machine Learning: A Bearing-Based Case Study Md Emon Ahmed
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.v1i02.1311

Prediction accuracy does not necessarily indicate whether a machine-learning model warns of an impending equipment event at an actionable time. This study evaluates a data-driven, explainable framework using 15 accelerated XJTU-SY bearing tests. We audited 9,216 dual-axis vibration files, constructed 16 leakage-checked features from 9,201 preterminal observations, and compared an RMS score, logistic regression, and random forest using five bearing-disjoint folds. For the primary ten-minute recorded-end-of-test horizon, mean fold average precision was 0.6491, 0.4194, and 0.3098, respectively. Nevertheless, new actionable alarm episodes occurred in only 4, 4, and 6 of 15 held-out bearings, with 9, 32, and 27 premature episodes. An exploratory operating-condition holdout reduced actionable coverage to 0, 3, and 3 bearings. TreeSHAP explanations on 449 observations showed heterogeneous between-bearing feature rankings (median pairwise Spearman correlation 0.488). Feature-group ablation further demonstrated divergence between predictive ranking and alarm timing. These findings support decision-oriented evaluation alongside conventional metrics, but concern laboratory test termination rather than independently verified first damage or demonstrated industrial savings.

From Risk Scores to Decision Value: An Evidence-Governed Business Analytics Framework Under Capacity Constraints 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.v4i02.1312

Organizations increasingly use machine learning to identify customers, transactions, suppliers, and projects that require attention. Yet a high predicted risk does not establish that an intervention will help, that its benefit exceeds its cost, or that the underlying evidence permits action. This paper develops an analytical framework that connects prediction, intervention value, evidence verification, and limited managerial capacity. The framework combines temporally governed data, calibrated risk estimates, bounded intervention effects, constrained optimization, and selective human review. Its central object is a decision record linking each proposed action to the information available at the decision time, the assumptions behind its expected value, and the authority required for execution. Analytical propositions establish when risk ranking agrees with value ranking, how uncertainty changes the intervention threshold, and when additional review has positive expected value. A fully specified six-case worked example demonstrates the distinction. With capacity for two interventions, risk ranking produces an assumed expected net value of −1 unit, whereas value-based selection produces 54 units. A common uncertainty allowance of 0.10 reduces the optimized lower-bound value to 34 units. These quantities are arithmetic consequences of stated assumptions, not estimates from an organizational experiment. The contribution is an auditable decision formulation and a prospective evaluation protocol that separates predictive performance from intervention effectiveness. The framework offers testable design principles for business analytics while identifying the causal, temporal, and organizational evidence required before deployment.

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Home Page

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Editorial Team

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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

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Abstract

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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