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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 39 Documents
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Search results for , issue "vol 6, no 2 (2026)" : 39 Documents clear
Reconnecting with Nature in the Built Environment: The Roles of Biophilic and Biomimetic Urban Design Awal Prasetyo; Hendro Prabowo; Wahyu Prakosa; Destri Maya Rani
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.v6i2.1790

Abstract

Integrating nature into urban design has become a crucial approach for creating healthy, sustainable, and livable cities. This paper explores the multidimensional benefits and distinct roles of two transformative frameworks: Biophilic Design and Biomimetics. While both are rooted in nature, they offer complementary pathways. Biophilic Design focuses on human well-being, systematically integrating natural elements, light, and materials into the built environment to strengthen the innate human-nature connection, thereby improving mental and physical health, reducing stress, and enhancing social cohesion. In contrast, Biomimetics is a technical approach that solves complex urban challenges by emulating nature's models, principles, and ecological systems. It leads to innovative, efficient, and sustainable solutions, such as bio-inspired ventilation systems and materials that enhance urban resilience. The paper argues that moving beyond superficial greening, the synergistic application of Biophilic (human-centered) and Biomimetic (performance-centered) strategies enables a fundamental transformation towards regenerative, resilient, and dignified human settlements capable of addressing the pressing challenges of global urbanization and climate change.
Workforce Unit Abstraction for Governing Hybrid Human and Artificial Intelligence Operations Gopal Yuvaraj
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.v6i2.1811

Abstract

Enterprise service organizations increasingly deploy artificial intelligence agents alongside human workers. Yet, incumbent workforce management frameworks remain anchored to a purely human labor model, rendering AI agents invisible to capacity planning, performance attribution, and governance enforcement. This article addresses that conceptual gap through a design science research methodology, introducing three constructs as reusable primitives for hybrid workforce platform design. The Workforce Unit Abstraction defines a unified seven-attribute operational schema applicable to both human workers and AI agents, enabling consistent representation across planning, scheduling, and governance systems. The Hybrid Capacity Model extends demand-to-supply planning across heterogeneous workforce pools, resolving a multi-objective allocation problem that simultaneously optimizes cost, quality, and risk constraints. Governance-bound autonomy constrains AI Workforce Unit actions within a five-level, policy-enforced autonomy ladder supported by six mandatory governance controls. Together, these constructs provide a coherent reference model that closes the documented gaps in hybrid workforce management, including scheduling inefficiencies of up to 28%, attribution failures in 68% of organizations, and governance ambiguity responsible for 61% of hybrid workflow failures. The framework establishes a principled vocabulary for designing enterprise service platforms that manage human and artificial intelligence labor responsibly, transparently, and at scale.
Semantic Condensation of High-Cardinality Time Series for LLM-Driven Observability Akila Balasubramanian
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.v6i2.1820

Abstract

Modern cloud observability platforms generate high-cardinality time-series data comprising thousands of distinct metric streams differentiated by service, endpoint, region, infrastructure component, and operational context. While this telemetry provides valuable insights into system behavior, its volume and complexity are inherently mismatched with the reasoning constraints of Large Language Models (LLMs), which operate within finite context windows and token budgets. When raw telemetry is passed directly to LLM-based observability assistants, four recurring failure modes significantly degrade analytical quality: token explosion that saturates context windows before signal-rich entries are reached, lossy truncation that discards diagnostically critical streams under arbitrary cardinality limits, incoherent analytical narratives arising from contradictory statistical summaries, and resolution gaps that obscure trend evolution, anomaly propagation, and change-point detection. These limitations reduce the effectiveness of LLM-assisted troubleshooting and hinder the practical adoption of AI-driven observability systems in large-scale cloud environments. To address these challenges, this paper introduces Semantic Condensation, a token-aware transformation framework designed to convert large-scale time-series telemetry into structured, semantically consistent summaries optimized for LLM-based reasoning. The proposed approach integrates vectorized statistical pre-analysis to efficiently identify anomalous behaviors, multi-signal importance scoring to rank Time Series Identifiers (TSIDs) according to diagnostic relevance, behavior-aware trend classification to capture temporal dynamics, and consistency enforcement mechanisms to prevent contradictory interpretations across generated summaries. Furthermore, an adaptive token-budget allocation strategy dynamically distributes descriptive detail based on diagnostic importance, ensuring that the most critical telemetry receives greater representational fidelity while maintaining strict token efficiency.Experimental evaluation conducted on high-cardinality observability workloads demonstrates that Semantic Condensation can process more than 10,000 time-series identifiers within production-grade latency requirements. Results show substantial reductions in token consumption compared with raw telemetry transmission, while simultaneously improving downstream LLM reasoning accuracy, anomaly interpretation, root-cause investigation, and troubleshooting effectiveness. These findings indicate that Semantic Condensation provides a scalable and practical foundation for next-generation AI-assisted cloud observability and operational intelligence systems.
Sustainable Exploration of Peat Water for Drinking Water Resilience in the Remote Community of Muara Enggelam Ansahar Ansahar; Irkhamiawan Ma’ruf; Bondansari Bondansari; Sherly Trifena Karundeng
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.v6i2.1748

Abstract

Peat water is an abundant raw water source in tropical peatlands; however, its use for drinking water is constrained by its acidic pH, high colour, elevated levels of natural organic matter (NOM), and microbial contamination, all of which increase the risk of carcinogenic disinfection byproducts when chlorinated. In Muara Enggelam Village, Kutai Kartanegara Regency, communities rely heavily on peat water and incur high costs to obtain clean water from neighbouring areas. This study aimed to design a laboratory-validated hybrid water treatment system capable of producing potable water from peat sources in remote communities. Water samples were collected from upstream, downstream, and an existing community treatment unit and analysed for physical, chemical, and microbiological parameters. Raw peat water exhibited acidic pH (5.11–5.66), high colour (176–235 TCU), turbidity (21.8–85.9 NTU), elevated iron concentrations (0.2–0.4 mg/L), and severe microbial contamination. The existing community system effectively reduced turbidity and iron, but further reduced pH, leaving residual colour and detectable bacteria. Based on laboratory results and process evaluation, a four-stage hybrid system is proposed, consisting of pH neutralisation with quicklime, controlled coagulation–flocculation, multistage filtration using quartz sand, activated carbon, and ion-exchange resin, followed by ultraviolet disinfection to prevent disinfection byproduct formation. System sustainability is supported through a village-owned enterprise management model and community willingness to pay of IDR 50,000–150,000 per month. The proposed system demonstrates that peat water can be transformed into a safe, resilient drinking water source for remote tropical communities through an integrated technical and institutional approach.
Analysis of Informatics Engineering Students’ Dependency Level on the Use of ChatGPT Using the Support Vector Machine Method Aswita Indah Luthfiana Hasibuan; Dahlan Abdullah; Kurniawati Kurniawati
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.v6i2.1813

Abstract

This study aims to analyze the level of dependency among Informatics Engineering students on the use of ChatGPT in academic activities using the Support Vector Machine (SVM) method. The research data were collected through questionnaires distributed to students of the Informatics Engineering Study Program at Universitas Malikussaleh from the 2022–2025 cohorts, involving a total of 400 respondents. The research indicators included usage intensity, duration of use, purpose of use, perceived effectiveness, and dependency behavior toward ChatGPT. The collected data underwent several preprocessing stages. including missing value checking. label transformation. data normalization using Min-Max Scaling. and dataset splitting into 80% training data and 20% testing data. The classification process was performed using the Support Vector Machine (SVM) algorithm with a linear kernel. The experimental results showed that the proposed SVM model successfully classified student dependency levels into three categories, namely low, medium, and high, achieving an accuracy of 95%, which indicates excellent classification performance. The findings also revealed that usage frequency, duration of use, and the utilization of ChatGPT for academic assignments and programming activities were the most influential factors affecting student dependency. Furthermore, a web-based system was developed using Python Flask and SQLite to facilitate data processing, model training, and visualization of classification results. The system testing results demonstrated that all implemented features functioned properly according to the specified requirements.
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.

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