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Real-Time Settlement Readiness for a Future T+0 Market Structure Pavana Kumar Chandana
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1849

The progressive compression of securities settlement cycles from T+5 to T+2, T+1, and ultimately the aspirational T+0 real-time settlement represents a fundamental transformation in modern financial market infrastructure. This evolution has been driven by regulatory reforms, advances in digital technologies, and the need to mitigate systemic vulnerabilities exposed during major financial crises. However, most existing post-trade infrastructures remain dependent on batch-oriented architectures that were designed for longer settlement cycles and are therefore unable to support the speed, scalability, and resilience required for real-time settlement environments. This study examines the architectural and operational requirements necessary to enable T+0 settlement through event-driven, cloud-native financial infrastructures. The proposed architecture integrates microservices, high-throughput messaging platforms, distributed processing, and automated operational workflows to support continuous, low-latency transaction processing with minimal downtime. In addition, the study emphasizes the importance of pre-trade readiness frameworks, intelligent exception management, and machine learning–driven decision support to replace the manual and interval-based operational processes commonly found in legacy T+2 settlement systems. While T+0 settlement significantly reduces counterparty credit exposure and settlement risk, it simultaneously introduces new operational, cybersecurity, and liquidity challenges due to compressed processing windows and increased dependence on real-time infrastructure. Furthermore, the study highlights that the benefits of T+0 settlement are influenced by market network topology. In particular, scale-free financial networks may derive fewer advantages from real-time settlement than traditionally interconnected markets, thereby affecting the effectiveness of central counterparty clearing mechanisms. The findings suggest that successful implementation of T+0 settlement requires more than technological modernization. It also demands coordinated regulatory reform, industry-wide infrastructure standardization, interoperable data governance, and carefully planned transition strategies that enable both large financial institutions and smaller market participants to adopt real-time settlement capabilities while maintaining financial stability, operational resilience, and long-term market efficiency

Predictive Talent Acquisition: AI Governance and Enterprise Workforce Intelligence Zeeshan Khan
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1841

Artificial intelligence (AI) has emerged as a transformative technology in enterprise talent acquisition, offering significant opportunities to improve recruitment efficiency through automated candidate screening, intelligent job matching, workforce analytics, and predictive hiring strategies. Organizations increasingly adopt AI-driven recruitment systems to reduce hiring costs, accelerate decision-making processes, and enhance workforce planning capabilities. However, despite these operational advantages, concerns remain regarding algorithmic bias, fairness, transparency, and regulatory compliance. This study investigates the balance between AI-enabled recruitment efficiency and the ethical, legal, and governance challenges associated with algorithmic decision-making in talent acquisition. A systematic review of 34 peer-reviewed studies spanning computer science, organizational psychology, human resource management, and legal scholarship was conducted to identify key trends, opportunities, and risks in AI-based recruitment systems. The analysis reveals the emergence of five levels of talent acquisition maturity, ranging from traditional applicant tracking systems and data-driven workforce acquisition to predictive talent acquisition and fully autonomous recruiting models. The findings indicate that advanced machine learning techniques, including XGBoost and Random Forest algorithms, can achieve predictive accuracies of up to 96% in employee attrition forecasting and workforce optimization tasks. Nevertheless, the study also demonstrates that such systems frequently inherit demographic and historical biases embedded within training datasets, potentially leading to discriminatory hiring outcomes when adequate oversight mechanisms are absent. Furthermore, the review identifies significant compliance challenges related to emerging regulations, including New York City Local Law 144, Illinois HB 3773, and the European Union AI Act. The findings suggest that sustainable AI-driven recruitment requires the integration of bias auditing frameworks, explainability mechanisms, human-in-the-loop governance, and continuous regulatory compliance monitoring. The study concludes that the long-term success of AI-enabled talent acquisition depends not only on technological performance but also on the ability to ensure fairness, accountability, transparency, and ethical decision-making throughout the recruitment lifecycle

When Pipelines Break Silently: The Case for Self-Adaptive Data Cleaning Siddharth Kumar Choudhary
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1829

Schema changes have transitioned from occasional maintenance events to a routine operational condition in modern data engineering environments. Yet, the theoretical foundations for handling it at the operator level remain underdeveloped. Upstream teams modify field names, remove columns, alter data types, and restructure nested objects as a natural consequence of product iteration and infrastructure migration. At the same time, downstream cleaning logic continues executing against structural assumptions that no longer hold. The result is pipelines that survive schema change while silently degrading the quality of their output in ways that standard monitoring frameworks do not immediately surface. Recent advances in schema evolution cataloging, data quality optimization, provenance tracking, and CI/CD governance have each strengthened individual components of the pipeline reliability problem. Still, they have treated these concerns as neighboring domains rather than as parts of a unified theory. The concept of operator robustness to schema evolution addresses this fragmentation directly by defining robustness not as functional continuity alone, but as the joint preservation of function and data quality after structural change. Mapping the cleaning operator's sensitivity to elementary schema modification operations yields a decision framework that transforms reactive maintenance into an analyzable adaptation problem. Embedding that framework within a self-adaptive architectural loop—monitoring, analysis, planning, and execution over shared knowledge—establishes the conceptual infrastructure for pipelines that respond to structural uncertainty autonomously rather than depending on manual repair. The forward agenda includes tri-objective optimization across quality, latency, and compute cost, provenance-guided operator repair, contract-aware adaptation policy, and empirical benchmarking against realistic long-running pipelines with controlled schema injection.

Privacy-Preserving Federated Query Processing Across Distributed Cloud Data Platforms Shankar das Boddu
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1844

The rapid adoption of multi-cloud data platforms has enabled organizations to perform large-scale distributed analytics while complying with regional data governance requirements. However, existing federated query processing frameworks often prioritize performance and interoperability without providing rigorous privacy guarantees or enforcing regulatory compliance across multiple jurisdictions. This study presents PrivFed, a privacy-preserving federated query processing framework designed for distributed cloud data platforms operating under heterogeneous legal and regulatory constraints. PrivFed integrates differential privacy (DP), secure aggregation (SA), and a region-aware multi-objective query optimizer to support compliant, low-latency analytics over geographically partitioned datasets without exposing sensitive information. The proposed framework formalizes the federated query compliance problem by jointly optimizing query execution cost, privacy preservation, and data residency requirements. Formal analysis establishes -DP guarantees under adaptive composition and proves the security of the aggregation protocol in the semi-honest adversarial model. A comprehensive prototype was implemented across AWS Redshift, Azure Synapse, and Google BigQuery spanning three regulatory regions to evaluate scalability, efficiency, and privacy performance. Experimental results demonstrate that PrivFed achieves a median query latency only 1.4× higher than conventional non-private federated query systems, substantially outperforming homomorphic encryption-based approaches that incur 18–340× latency overhead. Furthermore, privacy-aware predicate pushdown reduces inter-region data transfer by 62%, while maintaining a cumulative privacy budget of ? ? 1.0 across 10,000 simulated adaptive queries. Comparative evaluation against Presto, Trino, and BigQuery Omni indicates that PrivFed is the only framework capable of simultaneously satisfying three critical objectives: strict data residency compliance, mathematically provable privacy protection, and practical query execution with less than 2× performance overhead. These findings demonstrate that PrivFed provides a practical and scalable foundation for secure federated analytics in modern multi-cloud environments.

Public Sentiment Analisys on the Phenomenom of Body Shaming on Social Media X Using the Extreme Gradient Boosting Algorithm Nadya Raudathul Sofa; Dahlan Abdullah; Maryana Maryana
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1818

The phenomenon of body shaming on social media platform X (Twitter) has become increasingly widespread and has caused various psychological impacts on its victims. The high level of social media activity has made the spread of negative comments related to body shape more difficult to control. Therefore, a system capable of automatically performing sentiment analysis is needed to identify public opinions regarding this phenomenon. This study aims to implement the Extreme Gradient Boosting (XGBoost) algorithm in classifying public sentiment toward the body shaming phenomenon on social media X and to determine the sentiment analysis results obtained. The research data were collected using a web scraping technique through Tweet Harvest, resulting in 1,383 Indonesian-language tweets which were manually classified into three sentiment classes: positive, negative, and neutral. The text preprocessing stage included case folding, cleansing, tokenizing, normalization, and filtering without applying stemming, as it was proven to reduce model performance on social media text data. Feature weighting was carried out using the TF-IDF method, while the data were divided using an 80:20 ratio with the implementation of Random Over Sampling (ROS) to address class imbalance. The XGBoost model was built using parameters of n_estimators = 300, learning_rate = 0.05, and max_depth = 5. The evaluation results using a confusion matrix showed an accuracy value of 80.87%, with F1-scores of 0.85 for the negative class, 0.71 for the neutral class, and 0.81 for the positive class. The results indicate that the XGBoost algorithm is capable of classifying public sentiment toward the body shaming phenomenon with fairly good performance. In addition, a web-based sentiment analysis system was successfully implemented to facilitate the automatic and structured sentiment classification process.

Systematic Literature Review on AI-Enhanced Dual-Axis Solar Tracking Systems: Techniques and Performance Analysis Muhammad Basyir; Yuwaldi Away; Tarmizi Tarmizi; Ira Devi Sara
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1553

Recent advancements in artificial intelligence have significantly improved the performance of intelligent dual-axis solar-tracking systems, enabling more efficient photovoltaic (PV) energy harvesting under variable irradiance conditions, transient cloud cover, and mechanical uncertainties. This systematic literature review synthesizes 25 peer-reviewed studies (2018–2024) identified from Scopus and Web of Science using PRISMA 2020 procedures. We examine controller families (fuzzy logic, adaptive neuro-fuzzy interface system, deep reinforcement learning, and hybrid designs), sensing and actuation stacks (ephemeris, light sensors, inertial measurement, and computer-vision-based pose), and reported outcomes for tracking accuracy and energy gain. Across comparable conditions, AI-enabled controllers consistently reduce tracking error by ~10–35% and increase annual energy capture by ~8–25% relative to fixed-tilt or conventional rule-based/PID baselines, with the largest gains observed under partial shading and rapidly varying sky conditions. Validation is dominated by simulation, while prototype and hybrid (simulation plus field) evaluations—though fewer—provide stronger evidence of real-world robustness. Persistent challenges include computational cost on embedded hardware, sample-efficient learning and safety for field deployment, inconsistent reporting of metrics, and limited long-horizon testing. To address these gaps, we recommend (i) standardized benchmarking that reports tracking error, normalized energy yield, control latency and controller power, (ii) low-cost edge-AI implementations (model compression, quantization, and microcontroller-class deployment), and (iii) multi-season field trials with reproducible protocols across climates. The findings indicate a clear shift from static, hand-tuned control toward intelligent, adaptive methods. Hybrid designs emerge as a practical choice for deployment due to their combined stability and adaptability, whereas deep reinforcement learning shows state-of-the-art performance primarily in simulation and calls for careful simulation-to-real transfer. Overall, this review clarifies the evidence base and outlines priorities for fault-tolerant, low-cost, and real-time adaptive dual-axis solar tracking.

Boosting Creativity in Writing German Descriptive Texts with Project-Based Learning Anim Purwanto; Ninuk Lustyantie; Muhammad Kamal bin Abdul Hakim
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1585

This research is motivated by the low ability of students to write descriptive texts in German, which is largely caused by the use of conventional learning methods and limited active student involvement in the learning process. The aim of this study is to improve students’ writing skills through the implementation of PjBL that emphasizes collaborative and reflective project activities. This classroom action research was conducted in two cycles, involving 36 eleventh-grade students as subjects. Data collection techniques included observations of student activities and descriptive text writing tests, while data analysis was carried out using both quantitative and qualitative descriptive approaches by comparing learning outcomes between cycles. The results indicated a significant improvement in students’ writing performance: the average score increased from 65 in cycle I to 82 in cycle II, while the completion rate rose from 30.56% to 86.11%. Observational data revealed that students actively engaged in group discussions, confidently expressed ideas, responded to peer feedback, and demonstrated higher motivation in completing writing tasks. Furthermore, the quality of their descriptive texts improved, particularly in terms of structure, vocabulary, grammar accuracy, and creativity. These findings suggest that the PjBL model effectively enhances not only students’ descriptive writing skills but also their collaboration, critical thinking, and creative expression. The study theoretically contributes to the growing literature on PjBL in foreign language education, providing evidence of its impact on student learning outcomes. Practically, the findings serve as a valuable reference for teachers aiming to develop innovative, student-centered, and effective strategies for teaching descriptive writing in German, promoting active learning and sustained engagement in the classroom

Object-Based Migration Using SAP Landscape Transformation: A Framework for Real-Time Data Replication in S/4HANA Transformation Programs Srilekha Sangaraju
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1842

Enterprise transitions to SAP S/4HANA present significant data migration challenges, particularly when organizations must manage large volumes of transactional and master data across complex and heterogeneous system landscapes. As digital transformation initiatives accelerate, ensuring data integrity, migration speed, and business continuity has become a critical success factor in SAP modernization programs. Traditional migration approaches, including file-based Extract–Transform–Load (ETL) processes, Legacy System Migration Workbench (LSMW), and Business Application Programming Interfaces (BAPIs), often face limitations related to scalability, transformation complexity, reconciliation effort, and real-time synchronization requirements. This article examines SAP Landscape Transformation (SLT) as a comprehensive framework for object-based data migration, enabling selective, rule-driven replication with near-zero downtime capabilities during enterprise-wide SAP S/4HANA transitions. The study presents a structured five-phase implementation methodology encompassing discovery, configuration, build, mock migration cycles, and production cutover. In addition, the research analyzes SLT’s three-tier architecture, including Change Data Capture (CDC) mechanisms, transformation engines, and Mass Transfer ID (MTID) objects that support real-time replication and controlled migration execution. Evidence from a multi-country SAP S/4HANA transformation program in Latin America demonstrates that the SLT framework achieved throughput rates ranging from 25,000 to 40,000 records per hour for complex business objects, reduced cutover windows to less than 12 hours per country deployment wave, and improved master data completeness from below 80% to above 95%. Comparative evaluation against SAP BusinessObjects Data Services, SAP Migration Cockpit, and LSMW highlights SLT’s superior capabilities in handling real-time delta synchronization, selective object migration, and multi-wave deployment strategies. Furthermore, integration with SAP Master Data Governance (MDG) emerged as a critical quality multiplier, ensuring sustained data consistency, governance, and compliance beyond the migration lifecycle. The findings suggest that SLT provides a scalable, reliable, and governance-oriented approach for large-scale SAP S/4HANA transformation initiatives.

When Pipelines Break Silently: The Case for Self-Adaptive Data Cleaning Siddharth Kumar Choudhary
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1830

Schema changes have transitioned from occasional maintenance events to a routine operational condition in modern data engineering environments. Yet, the theoretical foundations for handling it at the operator level remain underdeveloped. Upstream teams modify field names, remove columns, alter data types, and restructure nested objects as a natural consequence of product iteration and infrastructure migration. At the same time, downstream cleaning logic continues executing against structural assumptions that no longer hold. The result is pipelines that survive schema change while silently degrading the quality of their output in ways that standard monitoring frameworks do not immediately surface. Recent advances in schema evolution cataloging, data quality optimization, provenance tracking, and CI/CD governance have each strengthened individual components of the pipeline reliability problem. Still, they have treated these concerns as neighboring domains rather than as parts of a unified theory. The concept of operator robustness to schema evolution addresses this fragmentation directly by defining robustness not as functional continuity alone, but as the joint preservation of function and data quality after structural change. Mapping the cleaning operator's sensitivity to elementary schema modification operations yields a decision framework that transforms reactive maintenance into an analyzable adaptation problem. Embedding that framework within a self-adaptive architectural loop—monitoring, analysis, planning, and execution over shared knowledge—establishes the conceptual infrastructure for pipelines that respond to structural uncertainty autonomously rather than depending on manual repair. The forward agenda includes tri-objective optimization across quality, latency, and compute cost, provenance-guided operator repair, contract-aware adaptation policy, and empirical benchmarking against realistic long-running pipelines with controlled schema injection.

Vectorized Enterprise Knowledge Systems: Transforming Organizational Intelligence Using Retrieval-Augmented AI Ananda Kumar Dey
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1845

Large-scale liquefied natural gas (LNG) projects represent some of the most logistically complex undertakings in industrial engineering, requiring the coordinated fabrication of tens of thousands of pipe spools across geographically distributed facilities while maintaining strict cost, schedule, quality, and regulatory requirements. Despite advances in digital manufacturing, limited research has examined how integrated material requirements planning (MRP) systems support synchronized fabrication at mega-project scale. This paper investigates the implementation of an integrated MRP framework at Fabrication Manufacturing facilities supporting LNG projects, where approximately 100,000 pipe spools were fabricated across six production facilities using a centralized planning and distributed execution architecture. The proposed framework aligns with Advanced Work Packaging (AWP) principles recommended by the Construction Industry Institute (CII), ISA-95 enterprise-control integration standards, and ASME/API engineering compliance requirements to enhance coordination between planning, procurement, inventory management, and shop-floor execution. The implementation introduces a bidirectional integration model between the MRP system and the Manufacturing Execution System (MES), enabling real-time synchronization of production schedules, material availability, fabrication status, and inventory data across all participating facilities. Furthermore, a centralized pre-buy material distribution strategy minimizes procurement uncertainty and improves supply chain responsiveness. Empirical results demonstrate an 18–25% reduction in procurement lead time, a 20–30% increase in fabrication productivity through AWP-aligned sequencing, and improved schedule reliability across multi-site operations. The integrated digital planning architecture also enhanced resource utilization, reduced production bottlenecks, and strengthened cross-facility coordination. These findings establish integrated MRP implementation as a critical enabler of efficient mega-scale LNG fabrication delivery and provide a practical, scalable, and replicable framework for engineering, procurement, and construction (EPC) contractors managing highly complex industrial projects

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Dahlan Abdullah

Contact Email
dahlan@unimal.ac.id

Phone
+62811672332

Journal Mail Official
ijestyjournal@gmail.com

Editorial Address
Jl. Tgk. Chik Ditiro, Lancang Garam, Lhokseumawe, Aceh - Indonesia, 24351

Location
Kota lhokseumawe,

Aceh

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Dahlan Abdullah

Contact Email dahlan@unimal.ac.id

Phone +62811672332

Journal Mail Official ijestyjournal@gmail.com

Editorial Address Jl. Tgk. Chik Ditiro, Lancang Garam, Lhokseumawe, Aceh - Indonesia, 24351

Location Kota lhokseumawe, Aceh INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Dahlan Abdullah

Contact Email
dahlan@unimal.ac.id

Phone
+62811672332

Journal Mail Official
ijestyjournal@gmail.com

Editorial Address
Jl. Tgk. Chik Ditiro, Lancang Garam, Lhokseumawe, Aceh - Indonesia, 24351

Location
Kota lhokseumawe,

Aceh

INDONESIA