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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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Design and Development of a Mini Excavator as Innovative Learning Media Abdul Tahir; Irdam Irdam; Jasman Jasman; Musakirawati Musakirawati
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.v6i3.1775

Abstract

The rapid advancement of mechanical and electronic engineering, particularly in heavy equipment and construction automation, requires innovative learning media that effectively bridge theoretical knowledge and practical implementation. Conventional instructional methods often provide limited opportunities for students to develop hands-on experience with complex mechatronic systems. This study presents the design, development, and evaluation of a miniature battery-electric excavator intended as an educational platform for teaching construction robotics, automation, and sustainable engineering principles. The development process followed a structured methodology consisting of conceptual design, mechanical fabrication, electronic integration, simulation, and functional testing. Unlike conventional excavators that rely on hydraulic actuators, the proposed system employs electric motors to drive the boom, arm, and bucket mechanisms, demonstrating an energy-efficient and environmentally friendly alternative. Locomotion is achieved using DC gearbox motors for wheel movement, while an independent DC motor enables 90° rotational motion. The control architecture is based on an Arduino Mega microcontroller integrated with a wireless PS2 joystick, providing intuitive and precise user control. Lightweight aluminum was selected for the backhoe assembly to reduce overall weight, whereas the main chassis was constructed from mild steel to ensure structural strength and operational stability. Experimental evaluation confirmed that the prototype successfully performed essential excavation motions, achieving a maximum horizontal reach of 1,400 mm, a vertical reach of 1,420 mm, an average operating speed of 0.5 m/s, and a maximum speed of 1.0 m/s. With compact dimensions of 800 × 600 × 750 mm, the system is safe, portable, and suitable for laboratory-based education. Furthermore, the platform supports Deep Neural Network (DNN) data generation and reinforcement learning research, providing a cost-effective and safe environment for advancing student competencies in intelligent construction technologies
Integrated Planning for Sustainable Clean Water and Sanitation at Mulawarman University Fahrizal Adnan; Febrina Zulya; Searphin Nugroho; Rahmahtriananda Faradilla; Aulia Fauziyah Luayi; Rizqi Nadhirawati; Budi Nining Widiarti; Ibrahim Ibrahim
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.v6i1.1888

Abstract

Clean water and sanitation are essential components of a healthy, safe, and sustainable campus environment. At Mulawarman University, increasing numbers of students, lecturers, staff, and other academic community members require adequate water supply and sanitation infrastructure to support educational, administrative, and daily activities. This study aims to develop a long-term plan for clean water provision and sanitation facilities based on projected population growth through 2048. The methodology includes population projection analysis, estimation of clean water demand, assessment of sanitation facility requirements, and planning for solid and liquid waste management. The results indicate that demand for clean water and sanitation infrastructure will increase consistently with the growth of the university population. At the end of the sixth planning period in 2048, the projected served population reaches 64,458 people. This population requires a clean water production capacity of 966.88 m³/day to meet projected demand. Solid-waste management requirements include ten temporary waste-storage facilities (TPS), three dump-truck fleets, and one integrated waste-treatment facility (TPST) with an estimated required area of 1,129.38 m². Meanwhile, wastewater management must accommodate a potential black-water discharge of 96.69 m³/day and grey-water discharge of 676.81 m³/day. Based on these projections, the study recommends developing an integrated clean-water distribution network, improving solid-waste collection and treatment facilities, and establishing an integrated wastewater-management system capable of accommodating future demand. The proposed planning framework is expected to support Mulawarman University in improving environmental quality, strengthening campus sanitation services, and achieving more sustainable infrastructure development. Furthermore, the findings provide a quantitative basis for phased infrastructure investment and long-term campus environmental management through 2048
An Explainable Optimization Framework for Demand-Driven Inventory Decisions Using SHAP and Mixed Integer Programming Alfry Aristo Jansen Sinlae; Fajriana Fajriana; Yenny Suzana; Kiki Puspo Arianty
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.v6i3.1882

Abstract

Demand uncertainty complicates inventory decision-making and requires decision-support systems that are both accurate and transparent. However, existing studies primarily emphasize either demand forecasting or inventory optimization, with limited attention to integrating explainability into a unified decision-making framework. This study develops and evaluates an Explainable Optimization Framework that combines Extreme Gradient Boosting (XGBoost) for demand forecasting, SHapley Additive exPlanations (SHAP) for model interpretability, and Mixed Integer Programming (MIP) for inventory optimization. The framework was developed following the Design Science Research methodology, encompassing problem identification, artifact development, demonstration, evaluation, and communication. Model performance was assessed using rolling-origin backtesting to provide a robust evaluation under dynamic and uncertain demand conditions. Forecasting accuracy was measured using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that XGBoost achieved the highest forecasting accuracy, with an RMSE of 43.87, MAE of 33.92, and MAPE of 7.61%. The forecasted demand was subsequently incorporated into the MIP optimization model, resulting in a 22.3% reduction in total inventory cost, an increase in service level from 91.2% to 96.8%, an improvement in fill rate from 89.7% to 95.4%, a 57.1% reduction in stockout frequency, and an increase in inventory turnover from 5.8 to 7.2 compared with the baseline approach. SHAP analysis identified historical demand, promotional activities, and product price as the most influential variables affecting demand predictions, providing transparent explanations that enhance managerial trust and support informed inventory decisions. Overall, the proposed framework demonstrates that integrating forecasting, explainability, and mathematical optimization into a unified decision pipeline significantly improves operational efficiency, inventory performance, decision transparency, and resilient data-driven supply chain management across diverse industrial sectors
Queue-Based Picking for Kitting Operations: Component Staging Architecture with Work Center-Integrated Execution in Warehouse Management Systems Praveen Kumar Yeruva
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.v6i3.1924

Abstract

Order picking is one of the most labour intensive warehouse operations‚ and picker travel activity accounts for an important proportion of order picking execution in fulfillment areas. In kitting operations that serve a family of manufacturing work centers‚ inefficient paper-based task initiation‚ manual sorting of wave output‚ and self-assignment of work to the picker before productive execution begins increase travel and other wasteful activities. A queue-based picking and work center routing method for value-added services (VAS) kitting operations is described for implementation in a warehouse management system (WMS). Printed pick label bundles and Kanban filing boards are replaced by a mobile execution process where warehouse orders are routed to named queues used for each work center. The pickers follow the system-directed pick path on their radio frequency (RF) terminals. The handling unit (HU) labels are printed on-demand after the task is completed on mobile printers. The proposed architecture only relies on standard WMS parameters: warehouse order creation rule (WOCR)‚ consolidation group control‚ queue determination logic‚ and stock removal control indicator (SRCI) and starts from the assumption that picker routing‚ workload balancing‚ warehouse digitization‚ and wave scheduling should be treated holistically. It is concluded that‚ by eliminating manual sorting dependencies‚ organizing the execution in accordance with work-centres‚ minimizing non-value-adding labor‚ and being able to perform multi-picker waves in parallel with system-directed task assignments‚ queue-based‚ work-centre-integrated picking provides increased visibility of a staging area‚ synchronized component release both on-site and off-site‚ and improved storage efficiency through quantity-based retrieval prioritization‚ providing a desirable design pattern for paperless kitting execution in modern warehousing operations
Architecting Reliable Knowledge Retrieval Systems Using Large Language Models Bharat Kumar Reddy Karumuri
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.v6i3.1908

Abstract

The increasing deployment of large language models (LLMs) in enterprise environments creates significant reliability challenges, including hallucination, factual inconsistency, limited knowledge traceability, uncertainty, and operational inefficiency. This study develops a literature-based architectural framework for reliable knowledge retrieval systems that separates external knowledge management from LLM-based reasoning and generation. The framework synthesizes key architectural mechanisms, including knowledge representation, hybrid retrieval, reranking, evidence selection, context construction, response verification, provenance tracking, uncertainty handling, guardrails, and computational efficiency. The resulting architecture organizes these mechanisms into coordinated layers that regulate the flow of external evidence from knowledge sources to generated responses while supporting traceability, evidence grounding, and controlled abstention when sufficient evidence is unavailable. The architectural synthesis further identifies complementary strategies for enterprise deployment, including semantic coaching, model routing, and human oversight, to balance reliability, scalability, responsiveness, and operational cost. The analysis indicates that reliable LLM deployment should be addressed as an end-to-end architectural challenge rather than solely as a model-performance problem. In this perspective, knowledge access, evidence quality, retrieval accuracy, verification, provenance, uncertainty management, and governance must function as integrated system components. The proposed framework provides a structured foundation for designing maintainable, auditable, reliable knowledge retrieval systems capable of supporting enterprise applications and other high-stakes environments. By explicitly separating knowledge acquisition, retrieval, reasoning, verification, and governance functions, the framework also facilitates modular implementation, systematic evaluation, and continuous improvement. Consequently, it offers practical architectural guidance for organizations seeking to deploy LLM-based systems while maintaining factual reliability, operational control, transparency, and accountability across evolving enterprise knowledge environments
Physics-Informed Graph Neural Network for Industrial Process Monitoring with Limited Sensor Availability Lutfiyah Dwi Setia; Dhesinta Arrova Dewi
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.v6i3.1920

Abstract

Reliable operation and decision-making in industrial process monitoring increasingly depend on sensor networks. However, monitoring accuracy can deteriorate when sensor availability is limited by equipment failures, communication interruptions, or deployment constraints, particularly in complex industrial environments. This study proposes a Physics-Informed Graph Neural Network (PI-GNN) framework for industrial process monitoring under incomplete sensor conditions. The proposed approach combines graph-based representation learning, which captures relationships among process variables, with physics-informed constraints derived from mass and energy conservation principles to improve prediction reliability and robustness. The framework was evaluated using Supervisory Control and Data Acquisition (SCADA) data collected from a palm oil mill in Aceh Utara, Indonesia. Sensor unavailability was simulated through random masking at missing-data rates of 20%, 40%, and 60%. Performance was benchmarked against Long Short-Term Memory (LSTM), Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Transformer models under identical experimental conditions. The results demonstrate that PI-GNN achieved the strongest prediction performance under moderate sensor loss, with an RMSE of 0.0785 and an MAE of 0.0532. Under severe sensor degradation, represented by a 60% missing-data rate, the proposed framework maintained an RMSE of 0.1297 and outperformed the Transformer baseline. Ablation analysis further demonstrated that the incorporation of physics-informed constraints contributed substantially to model robustness under incomplete sensor conditions. These findings indicate that integrating graph-based learning with domain-specific physical knowledge can provide a reliable approach for industrial process monitoring when sensor availability is constrained. The proposed framework therefore offers a promising foundation for resilient monitoring systems capable of maintaining predictive performance despite progressive sensor degradation and incomplete process observations in industrial environments
Analysis of The Effect of Cultural Tourism Development, Accessibility and Economic Policy on Tourism Competitiveness in Indonesia Ardiyanto Maksimilianus Gai; Tono Mahmudin; Vivid Violin; Ahmad Nur Budi Utama; Riesna Apramilda
International Journal of Engineering, Science and Information Technology Vol 4, No 2 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i2.485

Abstract

The goal of this research is to pinpoint tourism trends and patterns that can enhance visitor experiences and enhance the economic and social advantages for local communities. We conducted this qualitative research through interviews and surveys. The surveyed population included men and women from various age groups, economic work backgrounds, education levels, and social status. Architectural heritage has an important role in the development of cultural tourism in various countries, including Indonesia, because of the unique city layout, historical value, and aesthetics of its buildings. Various steps, including the development of architectural tours, exhibitions, workshops, and collaboration with local communities, carry out efforts to preserve and promote architectural heritage. We hope that by raising public awareness and appreciation of architectural heritage, current and future generations can continue to enjoy it, while also positively contributing to the strengthening of a region's cultural identity. Through sustainable efforts, architectural heritage can become a productive resource that supports the preservation and strengthening of local heritage and identity, so that cultural tourism development can provide significant economic and social benefits.
Environmental Engineering Urban Environmental Intelligence Framework for Air Quality Prediction Using Multi-Source Mobility and Climate Data Sondang Sibuea; Yohanes Bowo Widodo
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.v6i3.1916

Abstract

Rising urban mobility, intensive anthropogenic activity, and limitations in conventional monitoring systems capable of delivering continuous spatial coverage have turned urban air pollution into a pressing environmental concern. Existing air-quality prediction approaches tend to depend on single-source observations or are built primarily for large metropolitan areas, which limits how well they apply to secondary cities with localized pollution dynamics. To address this, the study introduces an Urban Environmental Intelligence Framework that brings together multi-source air quality, climate, spatial, temporal, and air-quality information for urban air-quality prediction. Evaluation of the framework was conducted using hourly observations of pollutant concentrations (PM2.5, NO2, and CO), mobility indicators (traffic volume, vehicle count, average speed, and congestion index), meteorological variables, and urban spatial attributes, collected from Lhokseumawe, Indonesia. A multi-source XGBoost model was benchmarked against Single-Source XGBoost, Random Forest, and LSTM models using a chronological data partition and multiple evaluation metrics, including MAE, RMSE, MAPE, and R². The proposed framework achieved the strongest predictive performance across all target variables evaluated, attaining an R² of 0.912 and an RMSE of 5.84 µg/m³ for PM2.5 prediction. Ablation analysis confirmed that temporal and mobility information contributed most substantially to prediction accuracy, while climate and spatial variables provided complementary contextual value. Feature interpretation further revealed that traffic intensity, historical pollutant levels, and meteorological conditions were the dominant drivers of urban pollution outcomes. Overall, the proposed framework offers an effective environmental approach for supporting short-term air-quality forecasting, pollution hotspot identification, and evidence-based urban environmental management in secondary cities
Retrieval-Augmented Large Language Model for Institutional Knowledge Management and Decision Assistance in Public Organizations Yohanes Bowo Widodo; Sondang Sibuea
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.v6i3.1904

Abstract

The increasing volume and complexity of institutional documents in public organizations create challenges in accessing reliable knowledge for administrative processes and evidence-based decision-making. Conventional knowledge management systems often rely on keyword-based retrieval, while standalone Large Language Models (LLMs) may generate inaccurate responses when processing domain-specific institutional information. This study proposes a domain-specific Retrieval-Augmented Generation (RAG) framework to enhance institutional knowledge management and AI-assisted decision support in public-sector organizations. The framework was developed using a Design Science Research approach with Universitas Malikussaleh as a case study. The proposed architecture integrates institutional knowledge base construction, semantic retrieval, grounded language generation, and source attribution mechanisms. A knowledge base comprising 416 official institutional documents was developed through document preprocessing, semantic chunking, embedding generation, and vector database indexing. The framework was evaluated using 200 institutional queries based on retrieval performance, response quality, explainability, and system efficiency metrics. The results demonstrate effective retrieval capability, achieving Precision@5 of 0.884, Recall@5 of 0.921, and Mean Reciprocal Rank of 0.895. Generated responses achieved 94.6% factual accuracy, 91.8% contextual relevance, and 96.5% source attribution accuracy, while the hallucination rate was reduced to 3.2%. Furthermore, the framework achieved an average response latency of 1.18 seconds, indicating practical feasibility for institutional applications. These findings demonstrate that integrating semantic retrieval with grounded LLM generation can improve knowledge accessibility, transparency, and reliability for AI-assisted decision support in public organizations. The proposed framework provides a practical foundation for trustworthy institutional knowledge services and supports more efficient, explainable, and evidence-based administrative decision-making across diverse institutional contexts
Uncovering Vocational Students' Internship Skills Instruments to Improve Employability in the Hospitality Industry Rina Febriana Hendrawan; Ivan Hanafi; Yeni Yulianti; Rita Patriasih
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.v6i2.1547

Abstract

Employee performance is a crucial determinant of success in the competitive hospitality industry. Vocational education institutions are therefore required to prepare students with technical expertise and employability competencies aligned with authentic workplace demands. This study aims to develop an industry-informed internship assessment instrument for evaluating vocational students’ employability-related competencies in the hospitality sector. A qualitative instrument-development design integrated a semi-systematic literature review, two rounds of Focus Group Discussions (FGDs), and preliminary expert review. The literature review synthesized 68 studies to establish the initial conceptual basis and candidate employability indicators. Industry consultation involved 20 human resource managers and training supervisors from 15 hotels in Jakarta, while five vocational education lecturers with internship supervision experience participated in the expert review. Qualitative content analysis was used to identify, refine, organize, and contextualize indicators according to hospitality workplace expectations. The final instrument comprises five domains: Communication Skills, Collaboration and Teamwork, Self and Character Development, Professional Ethics, and Creativity and Innovation, represented by 30 behavioral indicators assessed using a 10-point performance rating scale with behavioral descriptors. The development process resulted in substantive refinement, including replacing Problem Solving and Decision Making with Self and Character Development because vocational interns have limited authority to make independent workplace decisions. The study contributes an industry-informed and context-sensitive approach by translating broad employability concepts into observable workplace behaviors. The instrument provides a practical basis for internship supervision, formative feedback, curriculum alignment, and university–industry collaboration. Further psychometric validation is required to establish its reliability and construct validity.

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