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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
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Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 659 Documents
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Revolutionizing SQL Server Administration with dbatools: The Power of Automation in Modern DBA Practices Siva Kumar Raju Bhupathiraju
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.v6i3.1862

Abstract

The systematic nature of database administration in a large-scale SQL Server environment, along with the need for repeatability and accuracy, makes manual operational processes impractical. dbatools, an open-source PowerShell module for SQL Server administration, was born. It encodes best practices for managing all aspects of a SQL Server deployment and administration into a suite of composable, scriptable commands. In function, dbatools exposes many common or requested management processes through the SQL Server Management Objects layer, allowing instance migrations, backup and restores, Always On Availability Groups, security policy compliance, and performance audit checks to be completed without the use of fragile GUI interfaces or one-off scripting. The dbatools cmdlets are fully compatible with PowerShell pipeline composition, common parameters, and structured exception handling, and therefore compatible with enterprise automation solutions such as CI/CD and configuration management. Backup integrity checks that are typically deferred to manual procedures due to their resource-intensive nature are implemented as a schedulable, repeatable automated restore test command that tests recoverability against test instances. Security administration is improved by commands to audit permissions, enforce encryption policy, and generate reproducibly accurate compliance reports. With declarative commands and built-in parameter validation, increasingly common high-availability production configurations that previously required a series of manual commands that risked sequencing errors can now be created with a single command. This positions dbatools as a building block of modern SQL Server deployments, where the DBA evolves from reactive manual execution of admin tasks to policy-defined infrastructure governance as scale and deployment topology and complexity grow with the organization
Improving Sentiment Classification of Indonesian Skincare Reviews through Fine-Tuned IndoBERT and Data Augmentation Nadia Thahira; Ar Razi
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.v6i3.1835

Abstract

This study analyzes sentiment in customer reviews of local skincare serum products on the Tokopedia e-commerce platform using a fine-tuned IndoBERT model enhanced with data augmentation techniques. A total of 5,000 reviews were collected from ten local skincare brands through web scraping and labeled according to star ratings into three sentiment classes: Positive, Neutral, and Negative. The dataset exhibited extreme class imbalance, with the Positive class representing 94.66% of all observations, creating substantial challenges for minority-class recognition. The data were divided through stratified sampling into 70% training, 15% validation, and 15% test sets to preserve class distributions. To mitigate imbalance, back-translation from Indonesian to English and back to Indonesian, together with synonym replacement, was applied exclusively to minority classes within the training set. The IndoBERT-base-p1 model was subsequently fine-tuned using focal loss combined with class weighting and compared against a baseline model trained without augmentation. Experimental results show that the proposed model achieved 94.40% accuracy, a Macro F1-score of 61.43%, and a Weighted F1-score of 95.14%. Although the baseline model obtained higher overall accuracy of 97.47%, it completely failed to identify the Neutral class, producing an F1-score of 0.00%. In contrast, the proposed approach increased the Neutral F1-score to 23.53% and improved the Macro F1-score by 2.30 percentage points, demonstrating more balanced performance across sentiment classes. The resulting model was deployed as SerumSense, a web-based application developed using Streamlit and SQLite, supporting both single-review and batch sentiment analysis. Black-box testing across 20 functional scenarios confirmed that all application features operated successfully as intended. These findings demonstrate that combining IndoBERT fine-tuning, targeted data augmentation, focal loss, and class weighting offers a practical approach for improving minority-class recognition in highly imbalanced Indonesian e-commerce review datasets.
A Deterministic Framework for Incremental System Isolation: Securing Financial Integrity and Public Safety in Distributed Platform Architectures Sowjanya Puligadda
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.v6i3.1856

Abstract

As digital platforms continue to expand in scale, complexity, and regulatory oversight, maintaining strict separation between experimental development environments and live production systems has become both a technical requirement and a governance imperative. In highly regulated sectors, including financial services, transportation, healthcare, and critical national infrastructure, inadequate isolation between non-production and production environments can lead to regulatory violations, financial losses, security breaches, and service disruptions affecting end users. This paper proposes the Multi-Tiered Incremental Isolation Framework (MTIIF), a deterministic architectural model designed to establish verifiable physical and logical segregation between development, testing, staging, and production environments. The framework integrates three complementary architectural mechanisms: cryptographic tenancy propagation embedded within distributed tracing headers to ensure end-to-end environment identity, physical segregation of storage and event-stream infrastructures for high-risk services, and autonomous regulatory guardrails implemented through CI/CD-integrated linting, policy validation, and deployment verification tools. Unlike conventional isolation strategies that rely primarily on operational procedures, the proposed incremental adoption model enables organizations to prioritize protection of high-risk service domains while progressively extending isolation across the entire service mesh with minimal operational disruption. Evidence from industrial deployments demonstrates that the framework reduces testing-related production incidents to statistically negligible levels, eliminates cross-environment data contamination responsible for financial integrity failures, and provides cryptographically verifiable audit trails supporting compliance with international regulatory standards. Furthermore, the framework improves operational resilience, deployment confidence, and software governance by transforming environmental isolation into a mathematically enforceable architectural invariant rather than a procedural practice dependent on human discipline. These findings establish MTIIF as a scalable reference architecture for secure, compliant, and resilient cloud-native enterprise platforms supporting continuous software delivery
Industry-Specific Applications of Digital Technologies: Frameworks, Sector Adaptations, and Operational Implications Across Contemporary Industrial Domains Kishore Kolipaka
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.v6i3.1864

Abstract

Industry-specific applications have transformed general information technology capabilities into specialized digital solutions tailored to the operational, regulatory, and strategic requirements of individual industries. As organizations accelerate digital transformation, technology strategies have evolved from conventional business–IT alignment toward an integrated digital business strategy in which domain-specific adaptation is considered a fundamental design principle. This article examines how industry-oriented digital technologies create organizational value by addressing sector-specific workflow architectures, compliance requirements, and risk profiles. Using a comprehensive literature review, the study analyzes the implementation of cyber-physical systems in manufacturing, clinical decision support systems in healthcare, distributed ledger technology in financial services, smart grid technologies in energy management, and continuous delivery systems in information technology. The findings demonstrate that industry-specific technological configurations significantly improve operational efficiency, decision quality, regulatory compliance, organizational resilience, and competitive advantage when aligned with the unique characteristics of each sector. Furthermore, successful implementation depends not only on technological capabilities but also on organizational factors, including evidence-based decision-making, knowledge management, interdisciplinary collaboration, and continuous organizational learning. These capabilities enable organizations to effectively integrate digital innovation into business processes while adapting to evolving regulatory and market environments. The review also identifies persistent challenges related to interoperability, governance, cybersecurity, workforce readiness, and technology standardization that continue to hinder large-scale implementation across industries. Despite substantial progress, research gaps remain in developing design frameworks and implementation methodologies that systematically integrate technological innovation with industry-specific operational contexts. This study concludes that future research should focus on adaptive, scalable, and sector-oriented design approaches capable of addressing the diverse requirements of contemporary industries. Such frameworks will support sustainable digital transformation while enhancing long-term organizational performance, innovation capacity, and strategic competitiveness across multiple industrial domains
Breaking Data Silos in Biotech: Integration Architectures for Scalable GMP Operations Rishi Nareshbhai Lad
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.v6i3.1852

Abstract

Modern biotechnology organizations operate in one of the most complex data environments across contemporary industries. Biopharmaceutical manufacturing, clinical research, quality management, laboratory operations, and regulatory affairs continuously generate vast volumes of structured and unstructured data that must be exchanged accurately, securely, and traceably across multiple enterprise platforms. Fragmented architectures, in which Laboratory Information Management Systems (LIMS), Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP), and Quality Management Systems (QMS) function as isolated silos, create significant operational challenges, including duplicated data entry, inconsistent information, delayed decision-making, increased compliance risks, and reduced manufacturing efficiency. These limitations become increasingly critical as organizations expand production capacity and regulatory requirements become more stringent. This article examines why enterprise integration has evolved from a technical enhancement into a strategic capability for scalable biotechnology operations. Drawing upon recent literature in biomanufacturing informatics, digital transformation, process analytical technology (PAT), Industry 4.0, and enterprise integration, the study analyzes integration architectures, interoperability standards, cloud-enabled platforms, and data governance practices adopted by leading biotechnology organizations. It further evaluates the regulatory drivers associated with Good Manufacturing Practice (GMP), data integrity, electronic records, and end-to-end traceability that necessitate seamless information exchange across enterprise systems. Evidence synthesized from recent empirical studies indicates that integrated biomanufacturing platforms can reduce process deviation rates by up to 35%, shorten batch release cycle times by 20–40%, improve data accuracy, enhance production visibility, and strengthen regulatory compliance compared with fragmented environments. The findings demonstrate that successful enterprise integration requires not only advanced digital technologies but also standardized data governance, organizational change management, cybersecurity, workforce capability development, and continuous performance monitoring to achieve sustainable operational excellence, regulatory resilience, and long-term competitiveness in the biotechnology industry.
Predictive Labor Elasticity: Stochastic Demand Modeling in Front-Line Workforce Management Akash Srivastava
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.v6i3.1859

Abstract

Labor represents the largest controllable operating cost in retail organizations, yet workforce deployment in many enterprises continues to rely on deterministic scheduling models that are poorly suited to the stochastic demand patterns of modern omnichannel retailing. As customer demand is increasingly distributed across in-store shopping, click-and-collect, curbside pickup, home delivery, and third-party logistics platforms, static labor schedules based on historical averages become ineffective in responding to rapidly changing operational conditions. This article introduces the Probabilistic Labor Elasticity (PLE) framework, an adaptive workforce optimization model that dynamically aligns labor capacity with real-time demand signals through probabilistic forecasting. The proposed framework models customer arrivals using a non-homogeneous Poisson process to generate forward-looking demand estimates and quantify labor elasticity, enabling proactive workforce allocation before demand surges occur. In addition, the framework incorporates Cross-Functional Fluidity, which measures an organization's capability to redeploy employees across operational functions through cross-training, digital enablement, and decentralized decision-making. To further enhance operational responsiveness, the framework integrates AI-powered Mission-Based Task Orchestration, which continuously assigns employees to the highest-value operational tasks during each micro-interval of the business day. The resulting adaptive labor allocation minimizes idle time, reduces service bottlenecks, improves customer responsiveness, and increases operational flexibility compared with conventional scheduling approaches. Furthermore, the study introduces the concept of Marginal Labor Productivity as a quantitative indicator for evaluating the financial value generated through dynamic labor deployment, including avoided labor costs and recovered revenue opportunities. The proposed PLE framework provides retail managers with a scalable decision-support model for improving workforce utilization, operational efficiency, and profitability while supporting resilient, data-driven labor management in increasingly dynamic omnichannel retail environments
Adaptive Product Governance for AI-Mediated Customer Journeys: A Dynamic Framework for Requirements Fluidity and Governance Complexity in Enterprise AI Systems Vijayalakshmi Narasimhan
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.v6i3.1848

Abstract

The integration of generative artificial intelligence (AI), adaptive recommendation systems, and large language model (LLM)-powered conversational interfaces into enterprise digital products has fundamentally transformed how customers interact with products and services. Historically, customer journeys were modeled as stable, linear sequences of touchpoints that product teams could define, validate, and govern using conventional Product Development Lifecycle (PDLC) frameworks. However, AI-mediated customer journeys are inherently dynamic rather than static, continuously evolving through model retraining, adaptive personalization, user feedback, and shifting data distributions. This behavioral dynamism introduces persistent customer journey volatility that challenges conventional governance approaches and reduces the effectiveness of traditional product management practices. This paper formalizes the AI-Induced Instability Model, a four-stage conceptual framework describing how AI behavioral adaptation propagates into customer journey volatility, requirement instability, governance complexity, and execution friction throughout enterprise product development. Building upon this diagnostic model, the study proposes the Adaptive Governance Framework (AGF), a six-dimensional governance architecture that redefines product governance through velocity-matched requirement revalidation, hypothesis-driven product specifications, progressive capability streaming, continuous risk assessment, AI lifecycle governance, and dynamic portfolio management. The framework is designed to support organizations in maintaining alignment between rapidly evolving AI capabilities and business objectives while preserving governance, compliance, and delivery quality. The AGF was empirically evaluated through a 14-week enterprise e-commerce product governance program involving 14 concurrent AI-enabled product initiatives. The evaluation demonstrated a 43% reduction in requirement revalidation cycle time, a 37% decrease in backlog volatility, a 29% improvement in delivery alignment, and governance documentation completeness reaching 91%, compared with 54.2% under a traditional PDLC baseline. These findings demonstrate that adaptive governance significantly improves organizational responsiveness, decision quality, and execution consistency in AI-driven product environments. The proposed framework provides enterprise product managers, technology leaders, and governance practitioners with a practical, scalable, and quantitatively validated architecture for managing AI-mediated digital products operating in continuously evolving customer ecosystems while ensuring sustainable innovation, regulatory compliance, and long-term business value.
Governing Before the Bid: The Prequalification-Enforced Reverse Auction Framework for ERP-Embedded Public Sector Procurement Vijaya Bhaskar Reddy Saadhu
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.v6i3.1865

Abstract

Competitive price discovery through electronic reverse auctions depends on enforcing vendor eligibility before the auction process begins rather than after contract award. In many public-sector Enterprise Resource Planning (ERP) systems, however, vendor prequalification is still managed through manual administrative procedures instead of embedded system controls, creating compliance gaps that lead to post-award disqualifications, procurement delays, audit findings, and inconsistent procurement outcomes. This article introduces the Prequalification-Enforced Reverse Auction (PERA) framework, a three-layer architectural model that embeds vendor prequalification directly into ERP reverse auction modules through user interface (UI)-level constraint mechanisms. The framework consists of Event-Level Lock, Participant-Level Lock, and Qualification-Level Lock, which collectively ensure that only vendors satisfying predefined eligibility requirements are permitted to participate in auction events. Vendor qualification is evaluated using a ten-dimensional Boolean assessment model organized into three governance domains: Technical and Operational Readiness, Financial and Commercial Governance, and Risk and Regulatory Control. To further improve procurement quality, the framework incorporates a price-signal diagnostic mechanism that analyzes preliminary pricing variance to identify potential specification ambiguity before auction creation. The proposed architecture was evaluated using implementation evidence from approximately 120 government agencies and more than 200 reverse auction events conducted within a state-level ERP procurement platform. The findings indicate that PERA substantially reduces post-award vendor disqualification incidents, improves regulatory compliance, standardizes vendor eligibility enforcement across agencies, and shortens procurement cycle times to approximately 60 minutes per auction event. By transforming prequalification from an administrative responsibility into an automated architectural constraint, the framework enhances transparency, fairness, auditability, and operational efficiency. The proposed PERA architecture provides a scalable and transferable design model for ERP-based public procurement systems without requiring changes to existing procurement regulations or upstream governance frameworks
Python's Pivotal Role in AI and Data Science Srinivas Sriram Mantrala
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.v6i3.1853

Abstract

Python has established itself as the dominant programming language in Artificial Intelligence (AI) and Data Science, fundamentally transforming how researchers, developers, and organizations address increasingly complex computational challenges. Its combination of simplicity, readability, flexibility, and an extensive open-source ecosystem has significantly accelerated AI research, application development, and industrial adoption across diverse sectors. This article presents a comprehensive review of Python's evolution as the primary programming language for AI and Data Science, examining the technical and organizational factors underlying its widespread acceptance. The study analyzes Python's rich ecosystem of libraries and frameworks, including NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, OpenCV, and Hugging Face Transformers, which collectively provide comprehensive support for data processing, machine learning, deep learning, computer vision, natural language processing, and predictive analytics. Furthermore, the article explores Python's advantages in rapid prototyping, cross-platform compatibility, cloud integration, reproducible research, and interoperability with high-performance computing environments. Evidence from recent academic literature and industry reports demonstrates that Python has substantially reduced development time, improved collaboration between multidisciplinary teams, and democratized access to advanced AI technologies by lowering technical barriers for students, researchers, and practitioners. The review also discusses emerging application domains in which Python continues to drive innovation, including AutoML, reinforcement learning, edge AI, generative AI, explainable AI, and AI-powered scientific computing. Despite challenges related to execution speed and memory efficiency compared with compiled languages, Python remains the preferred language due to continuous ecosystem improvements and seamless integration with optimized computing libraries. The findings confirm that Python has become the universal programming language connecting human creativity, data-driven decision-making, and intelligent machine systems, thereby reinforcing its strategic role in the future development of Artificial Intelligence and Data Science.
Prediction Model of Empty Fruit Bunch Production at PKS Sawit Hulu, Langkat Regency Based on Historical DataUsing the Arima Method Mayah Sapriani; Muchlis Abdul Muthalib
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.v6i2.1819

Abstract

This study aims to develop a forecasting model for Empty Fruit Bunch (EFB) production at PKS Sawit Hulu, Langkat Regency, using the Autoregressive Integrated Moving Average (ARIMA) method based on historical production data. The dataset consists of daily time series data collected from 2021 to 2025. The research process includes data collection, preprocessing, data cleaning, stationarity testing, parameter identification using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), ARIMA model development, forecasting, and model performance evaluation using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The evaluation results show an MSE value of 4,807,984,891.58 and a MAPE value of 29.32%. Based on model comparison and residual diagnostic testing, ARIMA (1,0,1) was selected as the best model because it produced lower error values and satisfied the white noise assumption. These results indicate that the model is adequate for representing the pattern of EFB production data and can be used for short-term forecasting. The forecasting results also show that EFB production fluctuates over time in line with changes in the amount of Fresh Fruit Bunches (FFB) processed during each period. These fluctuations are influenced by operational conditions, processing activities, and the availability of raw materials. The developed model is expected to assist PKS Sawit Hulu in production planning, waste management, storage capacity planning, biomass utilization, and operational decision-making. Overall, the ARIMA model provides a practical, systematic, and reliable approach for forecasting industrial production data based on historical patterns accurately.

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