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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
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The journal covers all aspects of applied engineering, applied Science and information technology, that is: Engineering: Energy Mechanical Engineering Computing and Artificial Intelligence Applied Biosciences and Bioengineering Environmental and Sustainable Science and Technology Quantum Science and Technology Applied Physics Earth Sciences and Geography Civil Engineering Electrical, Electronics and Communications Engineering Robotics and Automation Marine Engineering Aerospace Science and Engineering Architecture Chemical & Process Structural, Geological & Mining Engineering Industrial Mechanical & Materials Science: Bioscience & Biotechnology Chemistry Food Technology Applied Biosciences and Bioengineering Environmental Health Science Mathematics Statistics Applied Physics Biology Pharmaceutical Science Information Technology: Artificial Intelligence Computer Science Computer Network Data Mining Web Language Programming E-Learning & Multimedia Information System Internet & Mobile Computing Database Data Warehouse Big Data Machine Learning Operating System Algorithm Computer Architecture Computer Security Embedded system Coud Computing Internet of Thing Robotics Computer Hardware Information System Geographical Information System Virtual Reality, Augmented Reality Multimedia Computer Vision Computer Graphics Pattern & Speech Recognition Image processing ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 697 Documents
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From Infrastructure to Intelligent Enterprise: Best Practices and Strategic Frameworks for Migrating SAP Applications to Microsoft Azure Vimaladhithan Salem Marimuthu Rajagopal
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.v6i3.1919

Abstract

Enterprise organizations operating SAP landscapes face a critical inflection point as cloud adoption accelerates and legacy system maintenance windows continue to narrow. Migrating SAP workloads to Microsoft Azure has emerged as a strategic response that extends beyond infrastructure modernization toward broader business transformation. This article examines the methodologies, architectural frameworks, security principles, and financial justification models that collectively influence the success of SAP cloud migration programs. Drawing on established industry frameworks, Azure’s SAP-optimized infrastructure capabilities, and practitioner-informed migration methodologies, the article presents a structured approach to cloud transition that treats assessment, design, execution, and optimization as interdependent disciplines rather than sequential activities. Attention is given to zero-trust security architecture, governance automation, and post-migration innovation, which are frequently underinvested despite their significant influence on long-term operational outcomes. The article further explores how Azure’s native integration with artificial intelligence and analytics services can position migrated SAP environments as foundations for intelligent enterprise operations, enabling organizations to derive additional value from modernized technology landscapes. By connecting technical migration activities with business objectives, the proposed approach emphasizes the importance of aligning cloud architecture, security, governance, financial planning, and innovation strategies throughout the transformation lifecycle. Intended for enterprise architects, IT leaders, and senior decision-makers, this work provides a unified reference for organizations seeking to navigate SAP cloud transformation effectively. It bridges the gap between technical execution and strategic business value by emphasizing measurable outcomes, operational resilience, security, scalability, and continuous optimization as essential components of successful SAP migration programs in dynamic enterprise environments.
Effect of Drying Methods on the Organoleptic Properties and Water Activity of Mango Fruit Leather Reno Irwanto; Mulyati Mulyati; Bimo Kuncoro Jati; Jaka Marsita; Nada Fatin; Fatihah Fathinah Angjanina
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.v6i2.1555

Abstract

Drying conditions can affect both consumer acceptance and water activity (aw), which influences fruit leather stability. This study compared direct sun drying, convection oven drying, and food dehydrator drying for Indramayu mango (Mangifera indica L.) fruit leather. A single-factor Completely Randomized Design was applied using three independent production replicates. Mango puree was dried under direct sunlight at approximately 35 °C for 8 h per day, in a convection oven at 50 °C for 9 h, or in a food dehydrator at 55 °C for 8 h. Sensory acceptance of color, aroma, texture, taste, and overall liking was evaluated by 48 untrained panelists using a 4-point hedonic scale. Water activity was measured in triplicate at 25 °C. Data were analyzed using one-way analysis of variance (ANOVA), followed by Duncan’s multiple range test at ? = 0.05. The drying method significantly affected color (F(2,141) = 18.29, p 0.001, ?² = 0.206) and texture (F(2,141) = 15.48, p 0.001, ?² = 0.180), whereas aroma, taste, and overall acceptability did not differ significantly among treatments. The food dehydrator produced the highest scores for color and texture, while oven drying yielded the highest numerical taste score. Water activity also differed significantly among drying methods (p 0.001), with sun drying producing the lowest value (0.520 ± 0.010). Overall, forced-air dehydration favored sensory quality, particularly color and texture, whereas sun drying achieved the lowest aw. These findings indicate that drying-method selection should be based on the intended balance between sensory quality and product stability
Scalable AI-Driven Web Data Extraction Systems: Design and Implementation of an Enterprise Market Analytics Scraping Assistant Arun Mallur Chandrashekar
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.v6i2.1950

Abstract

Maintaining high-quality and continuously updated web data feeds remains a major operational challenge for enterprise market analytics platforms. Conventional rule-based web scrapers are increasingly brittle when confronted with dynamic website layouts, anti-bot countermeasures, and heterogeneous data sources at production scale. This paper presents the design and implementation of a scalable AI-driven web data extraction system deployed in production at RealPage, a leading enterprise real estate technology company. The proposed architecture integrates large language model (LLM)-guided DOM interpretation, a hybrid template-and-AI extraction pipeline, a self-healing failure recovery subsystem, and serverless orchestration using Microsoft Azure Functions to support continuous extraction across more than 50,000 property listing websites daily. The system reduces manual maintenance overhead through adaptive selector regeneration and automated failure classification, while a mandatory JSON Schema validation gate mitigates hallucination risks associated with LLM-generated extraction logic. Evaluation under sustained production conditions demonstrates high extraction accuracy across established domains, scalable throughput under bursty workloads, and significant cost efficiencies compared with equivalent virtual-machine-based infrastructure. The system also demonstrates effective automated recovery from the predominant failure mode, namely layout drift caused by website redesigns. The findings indicate that combining LLM-based semantic interpretation with deterministic validation and automated recovery can improve the reliability, scalability, and maintainability of enterprise web extraction systems. The broader contribution is a validated reference architecture for enterprise-grade AI-augmented web extraction that can be adapted to other domains requiring continuous, large-scale collection of structured information from heterogeneous, dynamic, and frequently changing web sources while reducing operational intervention and improving long-term system resilience in production environments
Intelligent Enterprise Integration Architecture for Autonomous Supply Chain Ecosystems Sandeep Reddy Varakantham
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.v6i2.1951

Abstract

Enterprise supply chains spanning ERP, WMS, TMS, MES, B2B/EDI platforms, and IoT event sources face a fundamental integration challenge that becomes more critical as operations move toward autonomous decision-making. Autonomous supply chain systems require data that are consistent, current, semantically aligned, and resilient to integration failures, whereas conventional enterprise architectures were largely designed for human-mediated workflows in which planners and operators could identify and correct inconsistencies before they propagated. This study proposes the Intelligent Enterprise Integration Architecture (IEIA), a framework designed to establish an integration foundation for autonomous supply chain operations. IEIA is structured around four mutually reinforcing principles: API-led connectivity, canonical data model governance, event-driven orchestration, and fault-tolerant pipeline design. The framework addresses four integration failure modes that can compromise autonomous decisions: silent data corruption, cascade pipeline failure, duplicate message processing, and schema drift. API-led connectivity establishes stable and versioned interfaces across heterogeneous enterprise systems, while canonical data governance provides semantic consistency across ERP, WMS, TMS, B2B/EDI, and IoT data sources. Event-driven orchestration improves data freshness for high-frequency operational signals while preserving centralized coordination for transaction-dependent processes. Fault-tolerant pipeline design incorporates idempotency, deduplication, dead-letter queues, and integration observability to support reliable processing under distributed-system failures. The framework is further mapped to Oracle Integration Cloud (OIC) capabilities and a phased implementation strategy is proposed to support progressive adoption without requiring immediate replacement of existing enterprise systems. The study demonstrates that autonomous supply chain capability is constrained not only by the intelligence of decision algorithms but also by the architectural reliability of the integration layer supplying their operational data. IEIA therefore provides a practical architectural foundation for scalable, resilient, and progressively autonomous enterprise supply chain operations
Enhancing TPACK through Collaborative Practices: Evaluating the Impact of Vocational Teacher Collaboration on Technological Integration Arum Dwi Hastutiningsih; Sugiyono Sugiyono; suyanto suyanto; Retna Hidayah; Amat Amat Jaedun Jaedun
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.v6i2.1509

Abstract

This study explores the role of collaborative practices in enhancing Technological Pedagogical Content Knowledge (TPACK) among vocational teachers in Yogyakarta, Indonesia, and identifies barriers to effective technology integration. The research involved 30 vocational teachers with leadership roles and teaching experience of more than five years, representing a critical group in advancing technology-supported vocational education. A quantitative approach was used, combining survey data on the frequency of barriers with descriptive analysis of collaborative practices. The results indicate that while many teachers demonstrate meaningful engagement in collaborative practices, participation is uneven and often constrained by limited time, inadequate access to technological resources, and misalignment between curriculum and technology. More than half of the teachers reported frequently experiencing barriers to technology integration, with the most significant challenges being heavy workloads, insufficient training, and infrastructure limitations. Nonetheless, the findings also reveal promising practices, as some teachers actively eng age in peer collaboration and professional development, highlighting the potential of structured support systems. The study concludes that although collaboration among vocational teachers is progressing, systemic gaps remain that hinder equitable and consistent TPACK implementation. To address these challenges, stronger institutional frameworks are required, including professional learning communities, continuous professional development, curriculum adaptation, and enhanced collaboration with higher education institutions. Such measures would provide sustained support, improve teachers’ technological competence, and ensure that vocational education is better positioned to meet the demands of the digital era.
A System-Level Framework for Quantifying and Mitigating Electromagnetic Interference in More-Electric Aircraft Selvam Rajendran
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.v6i2.1948

Abstract

The transition toward more-electric aircraft (MEA) architecture has fundamentally transformed the onboard electromagnetic environment, creating challenges that extend beyond traditional equipment-level electromagnetic compatibility (EMC) qualification. Contemporary aircraft integrate high-power electronic converters, distributed power generation, advanced digital avionics, and densely packaged subsystems within severely weight- and space-constrained airframes. While these technologies provide operational benefits, they also generate complex Electromagnetic Interference (EMI) phenomena that conventional component-level testing cannot fully characterize. This paper presents a system-level framework for quantifying EMI exposure and evaluating mitigation strategies in MEA. The framework combines source characterization, installation-dependent coupling path analysis, and functional susceptibility assessment to enable structured, margin-based evaluation of system-level EMC. Four normalized metrics are introduced: the Source Severity Index (SSI), Coupling Strength Ranking (CSR), Functional Margin Ratio (FMR), and Mitigation Effectiveness Factor (MEF). Together, these metrics provide quantitative tools for identifying dominant EMI contributors, characterizing worst-case coupling configurations, assessing functional robustness margins, and objectively comparing alternatives to mitigation. The framework explicitly addresses installation-driven EMC degradation mechanisms, including cable routing geometry, parasitic inductance, bundled harness mutual coupling, and production discontinuities. Application examples demonstrate that mitigation of coupling paths can provide the highest improvement in functional margin, particularly in installation-dominated EMI scenarios. In the evaluated case, coupling path mitigation achieved an MEF of 2.67, corresponding to approximately 8.5 dB improvement. These findings demonstrate the value of system-level, quantitative EMC assessment for MEA architectures and provide a structured basis for prioritizing mitigation actions, improving electromagnetic robustness, and supporting more reliable integration of advanced electrical and electronic systems in aircraft
Congestion-Aware RPC Scheduling for Tail-Latency Reduction in Hyperscale Distributed Systems Chirabrata Senapati
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.v6i2.1949

Abstract

Remote Procedure Call (RPC) frameworks form the connective substrate of modern distributed systems, enabling coordination across microservices, storage engines, recommendation pipelines, search infrastructure, and machine learning inference services deployed at hyperscale. As distributed systems grow in scale and heterogeneity, average latency increasingly fails as a performance adequacy metric. User-facing reliability is governed by tail latency, particularly at the 95th, 99th, and 99.9th percentiles. A single slow RPC in a fan-out request can dominate end-to-end response time, making tail latency behavior a first-class engineering concern for systems operating at the scale of modern cloud infrastructure. This paper presents Congestion-Aware RPC Scheduling (CARS), a distributed scheduling framework that dynamically routes, prioritizes, delays, retries, and hedges RPCs based on real-time multi-layer congestion telemetry. Unlike conventional RPC load balancing, which relies primarily on endpoint health metrics and recent latency observations, CARS integrates congestion signals from endpoints, application queues, transport layers, and network paths into a unified congestion scoring function. The scheduling objective is not merely to identify healthy endpoints but to identify execution paths least likely to violate request-level tail-latency objectives under current congestion conditions. CARS introduces three main contributions: a unified congestion scoring model combining five categories of multi-layer telemetry signals into a single replica scoring function; a deadline-aware scheduling algorithm routing requests based on estimated tail-latency risk relative to remaining deadline budgets; and adaptive hedging and admission control mechanisms that suppress duplicate work when congestion cost exceeds expected latency benefit. Experimental evaluation using trace-driven simulation across microservice fan-out workloads demonstrates that CARS achieves approximately 43 percent improvement in 99th-percentile latency and 49 percent improvement in 99.9th-percentile latency relative to exponentially weighted moving average latency-based routing

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