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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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Hybrid and Multi-Cloud Storage Strategies for SAP S/4HANA Migration: Architecture, Optimization, and Experimental Evaluation Maheswar Reddy Byreddy
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.v6i2.1816

Abstract

Enterprise resource planning (ERP) software systems such as SAP S/4HANA may have problems with storage efficiency‚ latency‚ and data resilience when deployed in a public cloud? Hybrid and multi-cloud infrastructures allow organizations to utilize on-premise infrastructure on-site in combination with a distributed public cloud infrastructure for improved performance‚ cost‚ and regulatory compliance? This paper introduces a workload-aware storage framework for SAP S/4HANA migration across hybrid and multi-cloud environments? It proposes a three-level architecture for clever data-tiering based on the classification of access patterns‚ adaptive workload placement based on SLA constraints‚ and cross-cloud orchestration with multi-objective optimization of storage cost‚ access latency‚ and operational risk in SAP system migration? Evaluation with large-scale synthesized SAP workloads matching published transactional‚ analytical‚ and archival access patterns shows a reduction in storage cost by up to 34%‚ reduction in access latency by up to 28%‚ and a more reliable system under simulated provider failure patterns‚ compared to static single-cloud and hybrid baselines? We find that storage-layer optimization is an important and under-explored dimension of enterprise cloud transformation strategy? 
Deterministic EtherCAT-Based Control Architectures for High-Precision Semiconductor Manufacturing Systems Utkarshkumar Shah
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.v6i2.1821

Abstract

Semiconductor manufacturing equipment demands deterministic real-time control architectures capable of synchronizing multi-axis motion, power delivery, and process sensing within microsecond-level timing windows to achieve the precision required at advanced technology nodes. EtherCAT, an industrial Ethernet protocol designed for hard real-time fieldbus communication, provides the deterministic, low-jitter communication fabric necessary to meet these stringent timing requirements across distributed embedded control nodes. This paper presents the design and implementation of an EtherCAT-based control architecture for high-precision semiconductor manufacturing systems, with a specific focus on RF impedance matching control deployed on a heterogeneous system-on-chip platform integrating the Xilinx Zynq-7000+ SoC. The proposed architecture implements a precision closed-loop control system that dynamically regulates RF voltage by actuating variable capacitors via stepper motor drivers with optical encoder feedback, enabling deterministic EtherCAT-controlled, synchronized, and independent actuation modes across multiple control axes. The Zynq-7000+ platform leverages its heterogeneous processing architecture — combining ARM Cortex-A9 application processors with FPGA programmable logic — to implement time-critical EtherCAT slave communication and closed-loop control algorithms in hardware while managing higher-level coordination logic in embedded software. Experimental results demonstrate sub-microsecond cycle-time repeatability, RF voltage regulation accuracy within 0.5% of the setpoint, and stable closed-loop tracking performance under dynamic impedance load variations representative of plasma etch and deposition process conditions. The architectural principles and implementation methodology established in this work provide a replicable framework for deploying deterministic EtherCAT-based control in semiconductor process equipment requiring distributed, high-precision motion and power regulation.
Typology of Old Houses in Kampung Arab Tanjung Selor, North Kalimantan Nur Asriatul Kholifah; Anisah Azizah; Putri Nopianti; Pandu K. Utomo; Kartika Tristanto; Ratri Bodromulatsih
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.v6i2.1789

Abstract

This study examines the typology of old houses in Kampung Arab Tanjung Selor, Bulungan Regency, North Kalimantan, a historic Arab settlement dating back to the 19th century. This settlement has historical value and unique old buildings, whose condition is deteriorating due to rapid development, requiring preservation efforts. Therefore, this study aims to identify and classify the typology of old residential buildings in Kampung Arab to support its development as a cultural heritage area. The method used is a qualitative descriptive, typological approach, in which data are collected through field observations, interviews with community leaders and elders, and documentation. The objects of study were three old houses believed by local community leaders to have been built during the arrival of the Arabs in Tanjung Selor, namely the houses of Salim bin Djoemaan, Umair Al Hasyim, and H. Muhamad Bansir, all of which are more than 100 years old and still retain their original structures. The results of the study show that these old houses share architectural similarities with Malay architecture and adopt the stilted structure of Kalimantan vernacular architecture as an adaptation to the swampy environment. The house's floor plan is rectangular, symmetrical, and clearly divided into public zones (veranda, living room), private zones (family room, bedrooms, dining room), and service zones (kitchen, bathroom). The separation of these spaces demonstrates the high value placed on privacy. Overall, these old houses embody Islamic values such as efficiency, egalitarianism, privacy, and local wisdom.
Application of Autonomous Solar Energy-Powered Fishing Small Boat to Support Fisheries Food Security in Underserved Villages ff Demak Regency Adenanthera Lesmana Dewa; Erika Saraswati; Malikus Sumadyo; Purwanto Purwanto; Muhammad Ikhsan Setiawan; Che Zalina Zulkifli; Mohd Fauzi Sedon; Fazilat Kodirova
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.v6i2.1822

Abstract

Fisheries-based food security in underserved coastal villages remains vulnerable due to limitations in fishing capacity, rising fuel costs, environmental degradation, and restricted access to modern fishing technologies. These challenges significantly affect the productivity and income of small-scale fishers, ultimately influencing the availability and affordability of fish as a primary source of protein for coastal communities. In many coastal areas of Indonesia, particularly in underserved villages, conventional fishing activities continue to rely heavily on fossil-fuel-powered boats, resulting in high operational expenses and increased greenhouse gas emissions. Consequently, there is a growing need for innovative and sustainable technological solutions that can improve fisheries productivity while supporting environmental conservation and community welfare. This study presents the design, development, and application of an autonomous, solar-powered small fishing boat aimed at enhancing sustainable fisheries production in underserved villages in Demak Regency, Indonesia. The proposed system integrates renewable energy harvesting through photovoltaic panels, autonomous navigation technology, fish location sensing, and intelligent route optimization to reduce dependence on fossil fuels while increasing operational efficiency and fishing effectiveness. A mixed-method approach was employed, combining engineering design, prototype development, laboratory validation, field deployment, and pilot testing involving local fishers. Stakeholder feedback was also collected to evaluate usability, acceptance, and potential socioeconomic impacts. The results indicate that the solar-powered autonomous fishing boat reduces operational fuel costs by approximately 95%, increases fishing efficiency by 42%, and decreases greenhouse gas emissions by an estimated 1.8 tonnes of CO? per vessel annually. Furthermore, the system demonstrates reliable navigation performance and contributes to safer fishing operations by reducing human workload and optimizing fishing routes. The adoption of this technology has the potential to increase fisher income, strengthen local food security, and support sustainable fisheries management. This innovation aligns with national strategies for renewable energy utilization, blue economy development, and rural economic empowerment, offering a scalable and environmentally friendly solution for coastal regions facing similar socioeconomic and ecological challenges.
Deep Learning-Based Mobile Application for Ornamental Plant Classification Muhamad Nur Gunawan; Syopiansyah Jaya Putra; Maulana Rifan Haditama
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.v6i2.1808

Abstract

This study aims to develop and evaluate a deep learning-based mobile application for the automated classification of ornamental plants, addressing challenges associated with visual similarity among species and environmental variability during image acquisition. The proposed system utilizes a Convolutional Neural Network (CNN) based on the MobileNetV2 architecture, selected for its lightweight structure and deployment efficiency on resource-constrained mobile devices. The dataset comprises approximately 600 images representing 10 ornamental plant classes, collected from real-world environments, and processed through a standardized preprocessing pipeline. Model training was conducted using the Teachable Machine platform over 100 epochs, with a batch size of 16 and a learning rate of 0.001, allocating 90% of the dataset for training and 10% for testing. Experimental results indicate that the proposed model achieves a classification accuracy of 96.3%, corroborated by evaluation metrics including accuracy curves, loss convergence, and class-wise performance analysis. The trained model was successfully converted into a lightweight format and integrated into an Android-based mobile application developed using the Flutter framework. Functional testing demonstrates that the application performs effectively in real-time classification scenarios, maintaining high accuracy and responsive on-device inference without relying on cloud computing. In conclusion, this study confirms that lightweight deep learning architectures, such as MobileNetV2, can be effectively implemented in mobile environments for ornamental plant classification. The proposed application enhances accessibility and usability, enabling rapid and accurate plant identification. Furthermore, this approach contributes to practical applications in horticulture, education, and biodiversity awareness, while demonstrating the feasibility of deploying efficient deep learning models on mobile platforms.
Platform Engineering as the Evolution of DevOps: An Architectural Framework for Enterprise-Scale Internal Developer Platforms Sudarshan T N
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.v6i2.1839

Abstract

The rapid adoption of microservices architectures and cloud-native technologies has significantly transformed modern software delivery practices while simultaneously increasing operational complexity across enterprise environments. Although DevOps methodologies have improved collaboration between development and operations teams and accelerated software release cycles, organizations operating at scale frequently encounter challenges such as fragmented tooling ecosystems, duplicated automation workflows, inconsistent infrastructure governance, and growing cognitive burdens on development teams. These challenges become particularly pronounced in enterprises managing hundreds of development teams and thousands of distributed services. In response, Platform Engineering has emerged as a strategic evolution of DevOps, introducing Internal Developer Platforms (IDPs) that provide standardized infrastructure capabilities, self-service automation, and enhanced developer experiences. This paper proposes a comprehensive architectural framework for enterprise-scale Platform Engineering environments that integrates Kubernetes-based container orchestration, GitOps-driven deployment models, infrastructure-as-code automation, service catalog management, observability platforms, and developer self-service portals. The study draws upon empirical findings from the DORA State of DevOps research program, the Cloud Native Computing Foundation (CNCF) Platforms White Paper, and established industry reference architectures to identify key design principles and operational requirements for successful platform adoption. The proposed framework introduces a four-layer reference architecture encompassing infrastructure, platform services, developer experience, and governance capabilities. In addition, a five-level capability maturity model is presented to guide organizations in systematically evolving their platform engineering practices. A comprehensive measurement strategy based on DORA performance metrics, platform adoption indicators, and developer experience metrics is also developed to assess operational effectiveness and business value. The findings demonstrate that Platform Engineering can substantially reduce operational complexity, improve software delivery performance, increase developer productivity, strengthen security and compliance governance, and enhance organizational scalability. The framework provides technology leaders with a practical roadmap for implementing sustainable and efficient cloud-native operating models in large-scale enterprise environments.
Not All Quality is Equal: Differential Data Quality Requirements for Operational Versus Strategic Decision-Making in the Energy Sector Mohan Kumar Dalai
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.v6i1.1828

Abstract

The accelerating digitalization of the energy sector has transformed data into a critical operational and strategic asset, enabling organizations to optimize performance, improve reliability, and support long-term sustainability goals. Despite the growing importance of data-driven decision-making, current industry practices frequently treat data quality as a universal objective, seeking to maximize all quality dimensions simultaneously regardless of context. This systematic synthesis of 50 empirical and conceptual studies published between 2012 and 2026 challenges this assumption by demonstrating that data quality priorities vary significantly according to decision-making requirements. The study employs a comprehensive literature review approach to examine how data quality dimensions influence operational and strategic decisions across diverse energy-sector applications. The analysis covers operational decision contexts, including real-time control systems, Supervisory Control and Data Acquisition (SCADA) platforms, fault detection mechanisms, predictive maintenance, and load-balancing operations, as well as strategic contexts such as capital investment planning, energy transition initiatives, regulatory compliance, risk management, and sustainability reporting. Findings reveal a clear divergence in data quality priorities. Operational decisions depend primarily on timeliness and accuracy to support rapid response and system stability, whereas strategic decisions place greater emphasis on completeness, consistency, and contextual integrity to ensure reliable long-term planning and governance. Based on these findings, this study proposes the Decision-Context Data Quality (DCDQ) Framework, which reorients data quality management from universal optimization toward context-sensitive prioritization. The framework provides technology managers, policymakers, and engineering practitioners with a structured methodology for aligning data quality investments with specific decision requirements, thereby reducing operational inefficiencies and financial costs associated with dimensional misalignment. Furthermore, the study highlights implications for data governance policies, engineering practices, and future research, emphasizing the importance of layered governance architectures, provenance-enabled data systems, and adaptive quality management strategies to support increasingly complex and data-intensive energy infrastructures.
Unveiling Ubud’s Gastronomic Image through Comparative Aspect-Based Sentiment Analysis as Cross-Platform Insights and Strategic SWOT Perspectives Ni Wayan Sumartini Saraswati; Ketut Jaya Atmaja; I Wayan Dharma Suryawan; Christina Purnama Yanti
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.v5i4.1843

Abstract

Gastronomy is a strategic element in developing tourism destinations, and Ubud occupies a position as a cultural center that offers culinary riches of traditional value. This research reveals the gastronomic image of Ubud through a cross-platform analysis of TripAdvisor restaurant reviews and public opinion on sentiment labels at the micro level, implemented with VADER before fine-tuning aspect sentiment analysis (with BERT) to obtain a more granular representation. An in-depth analysis was conducted using wordcloud for cross-platform insights and SWOT recommendations. The research results show that the highest accuracy model evaluation results reached 98.87% for the term extraction aspect and 97.48% for the sentiment analysis aspect for TripAdvisor. Based on insight analysis, the food aspect is the main focus on both platforms, but with different narrative patterns: TripAdvisor emphasizes detailed evaluations such as taste, presentation, and service, while X is more oriented towards promotions, cultural identity, and participatory experiences, especially related to events such as the Ubud Food Festival. The integration of ABSA results into the SWOT framework resulted in four strategic findings: main strengths in the form of culinary authenticity, service quality, atmosphere, and experiential value; weaknesses related to inconsistencies in taste and chef quality; opportunities obtained from organizing festivals, street food, and culinary diversification; and threats in the form of overtourism and the perception of high prices. Overall, this study provides conceptual, methodological, and strategic contributions. The resulting findings strengthen the scientific basis for developing Ubud as a sustainable gastronomic destination that emphasizes taste, nature, and culture, as well as supporting data-based decision-making for tourism stakeholders.
A Behavior-Centric Hardware-in-the-Loop Validation Framework for Cross-Domain Interaction Analysis in Automotive Chassis and Powertrain Systems Sana Fatima
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.v6i1.1834

Abstract

Modern automotive embedded control systems are increasingly characterized by multi-rate execution environments, distributed electronic control unit (ECU) architectures, and tightly coupled interactions among chassis, powertrain, braking, and vehicle stability subsystems. As vehicles evolve toward software-defined and highly automated platforms, ensuring system reliability requires validation approaches capable of capturing complex cross-domain interactions rather than evaluating individual components in isolation. Conventional Hardware-in-the-Loop (HIL) validation methodologies predominantly employ signal-centric verification strategies that assess input-output relationships of individual ECUs. Although effective for component-level testing, these approaches are insufficient for detecting emergent system behaviors arising from interactions among distributed controllers, communication networks, and shared control objectives. Such behaviors may include torque coordination anomalies, delayed stability responses, oscillatory control effects, and fault propagation across interconnected subsystems. This paper proposes a behavior-centric HIL validation framework that elevates system behavior to a primary validation artifact through formal representations based on temporal constraints, causal state transitions, and Signal Temporal Logic (STL) specifications. The framework integrates Functional Mock-up Interface (FMI)-based multi-domain co-simulation, observer-based residual generation, structured fault injection mechanisms, and real-time behavioral monitoring to systematically identify, quantify, and diagnose emergent behaviors during validation. Experimental evaluation was conducted using a dSPACE SCALEXIO HIL platform across 240 cross-domain test scenarios involving chassis and powertrain interactions under normal and fault conditions. The results demonstrate significant improvements over conventional signal-centric validation approaches, achieving a fault-detection coverage of 93.7% compared with 61.2% for the baseline method, a mean fault-detection latency of only 38 ms, and a temporal-constraint satisfaction rate of 91.4%. These findings indicate that behavior-centric validation provides a more comprehensive assessment of system-level dynamics and safety-critical interactions. The proposed framework transforms HIL from a component-verification environment into a behavioral-intelligence platform, offering automotive validation engineers a rigorous and scalable methodology for validating next-generation connected, autonomous, and software-defined vehicle architectures. 
AI Governance Across the Structured-to-Fluid Automation Spectrum: Deterministic and Agentic Architectures in Modern Contact Centres Vikas Prasad
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.v6i1.1825

Abstract

Determining the appropriate role of artificial intelligence (AI) within enterprise architecture presents a greater challenge than the technical deployment of AI itself. Modern contact centres operate across a wide spectrum of customer interactions, ranging from highly structured transactions that require strict compliance and auditability to complex conversations that demand contextual understanding and adaptive decision-making. This article proposes a governance-driven architectural framework based on a structured-to-fluid automation spectrum, which maps the operational characteristics of service interactions to the most appropriate automation and AI capabilities. Rather than adopting a technology-first approach, the framework emphasizes governance as the primary design principle, focusing on acceptable levels of operational variance, regulatory risk exposure, and the preservation of human accountability. The proposed framework integrates deterministic workflow execution, hybrid guardrail architectures, AI interpretation layers, asynchronous channel governance, agent augmentation capabilities, anomaly detection mechanisms, and comprehensive quality evaluation into a unified operational model. By positioning each interaction type along the automation spectrum, organizations can systematically determine where rule-based automation, human oversight, and AI-driven reasoning should be applied. This approach enables enterprises to balance efficiency, customer experience, compliance requirements, and operational resilience while reducing the risks associated with uncontrolled AI adoption. To support practical implementation, the framework incorporates a crawl–walk–run maturity progression that guides organizations through incremental stages of AI adoption. Enterprises can begin with tightly governed automation, expand toward AI-assisted decision-making, and ultimately evolve into agentic systems as governance capabilities mature. The framework provides a structured pathway for integrating AI into contact centre operations while maintaining the reliability, transparency, and accountability required in mission-critical service environments.

Page 62 of 70 | Total Record : 697

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