Technema: Journal of Intelligent Engineering and Computing
Technema: Journal of Intelligent Engineering and Computing is a peer-reviewed academic journal dedicated to publishing high-quality scholarly work in the fields of engineering, computer science, and emerging technologies. The journal provides an international platform for researchers, engineers, scientists, and technology practitioners to disseminate original research articles, theoretical contributions, experimental studies, system designs, and applied innovations that address contemporary challenges in intelligent systems and digital transformation.
Analysis of Customer Satisfaction and Priorities for Improving the Marketing Mix Using the Importance-Performance Analysis (IPA) Method and Potential Gain in Customer Value at Clauza Carwash
Competition in the vehicle-washing service industry requires businesses to continuously improve service quality to meet customer expectations. Clauza Carwash faces several customer concerns related to its marketing-mix attributes, which may affect customer retention and business competitiveness. This study aims to analyze the importance and performance of marketing-mix attributes and determine improvement priorities using the Importance-Performance Analysis (IPA) and Potential Gain in Customer Value (PGCV) methods. A quantitative approach was employed involving 100 respondents who completed a questionnaire covering 32 attributes based on the 7P marketing mix. Customer perceptions of service performance were assessed by comparing the importance and actual performance of each attribute. The IPA results identified 11 attributes as the main improvement priorities, particularly those related to product quality, price competitiveness, location visibility, employee performance, and service processes. The PGCV analysis further indicated that employee skills represented the highest improvement priority, with a PGCV value of 7.30, followed by price competitiveness, location visibility, material quality, and vehicle-shine performance. These findings indicate that the integration of IPA and PGCV can provide an objective basis for prioritizing service improvements according to customer perceptions. The results can support Clauza Carwash in developing targeted strategies to improve service quality, customer satisfaction, and competitive performance
Customer Satisfaction, Importance-Performance Analysis, Marketing Mix 7P, Potential Gain in Customer Value, Service Quality.
Copyright (c) 2026 Technema: Journal of Intelligent Engineering and Computing
Implementation of SHAP in Ensemble Learning for Interpreting Audit Risk Classification: A Preliminary Study Using a Public Audit Dataset
Ensemble learning models can achieve strong predictive performance on structured audit-risk data, yet their complex decision logic can limit transparency and technical accountability. This study develops a proof-of-concept Explainable Artificial Intelligence pipeline using SHapley Additive exPlanations (SHAP) to interpret Random Forest and XGBoost classifiers on the public UCI Audit Data. The experiment processed 776 observations and 27 columns, with one missing value in Money_Value imputed using the median of 0.09 and the non-numeric LOCATION_ID removed, resulting in 25 predictors and one binary target. A stratified 80:20 split with random_state = 42 produced 620 training observations and 156 test observations, with Random Forest achieving 1.0000 across accuracy, precision, recall, F1-score, and ROC-AUC, while XGBoost achieved 0.9936 accuracy, 1.0000 precision, 0.9836 recall, 0.9917 F1-score, and 1.0000 ROC-AUC. SHAP analysis identified Audit_Risk as the dominant global predictor, while its contribution to a representative at-risk prediction reached +5.82, shifting the raw model margin from −0.506 to 5.107 and yielding an estimated probability of approximately 0.994. However, because the dataset's target construction is closely associated with the Audit Risk Score, retaining Audit_Risk as a predictor introduces potential structural target leakage. The findings position the near-perfect performance as evidence of successful pipeline implementation and model interpretability rather than external generalization, supporting future leakage-controlled ablation, cross-validation, and external-data validation.
Audit Risk, Ensemble Learning, Explainable AI, SHAP, XAI.
The Impact of Enterprise Resource Planning (ERP) Software Implementation and System Quality on Service Quality at Oriskin Clinics (A Case Study of Three Oriskin Clinic Branches)
The digital transformation of healthcare service organizations requires integrated information systems capable of improving operational efficiency, data accuracy, and service quality. This study examined the effects of Enterprise Resource Planning (ERP) software implementation and system quality on service quality at three Oriskin Clinic branches located in Sentul, Karawang, and West Jakarta. An empirical quantitative design with an explanatory approach was employed. The study involved all 86 employees using the ERP system through a saturated sampling technique. Primary data were collected using a structured Likert-scale questionnaire and analyzed using validity and reliability tests, classical assumption testing, multiple linear regression, coefficient of determination, t-test, and F-test with IBM SPSS Statistics version 27. The results indicate that ERP software implementation has a positive and significant effect on service quality (β = 0.535, p = 0.001), while system quality also exerts a positive and significant effect (β = 0.409, p = 0.001). Simultaneously, both variables significantly influence service quality (F = 1841.430, p = 0.001) and explain 97.8% of its variance (R² = 0.978). These findings demonstrate that effective ERP implementation supported by high system quality substantially enhances operational performance and healthcare service quality in digitally integrated clinical environments.
Enterprise Resource Planning (ERP), Healthcare Service Quality, Multiple Linear Regression, System Quality, Web-Based Clinic.
Design and Development of a Web-Based Information System for Monitoring Mandatory Employee Training Using Firebase (Case Study: Pelni Hospital)
Hospitals are required to ensure that medical and non-medical personnel maintain valid competencies through periodic mandatory training to support patient safety and accreditation compliance. PT RS PELNI still relied on manual documentation of employee training and certification records, making it difficult to monitor compliance status, identify certificates approaching expiration, and obtain centralized information for managerial decision-making. This study aimed to design and develop the Mandatory Training Monitoring Information System (SIMON), a web-based application for managing employee data, mandatory training categories, training histories, and real-time certification monitoring. The system was developed using a Research and Development (R&D) approach with the Waterfall software development model, while HTML5, CSS3, and vanilla JavaScript were employed for the front-end and Firebase Authentication together with Cloud Firestore served as the back-end infrastructure. Functional validation was conducted using Black Box Testing to evaluate the performance of all system modules. The results demonstrated that user authentication, employee management, training management, training history recording, and dashboard monitoring operated successfully according to predefined functional requirements. The implementation of Firebase enabled real-time data synchronization, allowing certificate status to be updated instantly without manual intervention. The proposed system improves administrative efficiency, supports timely monitoring of employee training compliance, and provides a scalable cloud-based solution for competency management in healthcare organizations.
Cloud Firestore, Employee Competency, Firebase, Mandatory Training Monitoring, Web-Based Information System.
Redesigning the Facility Layout Using the Systematic Layout Planning (SLP) and Blocplan Methods to Minimize Material Transport Distances and Material Handling Costs at CV. Sidomulyo Perkasa
CV. Sidomulyo Perkasa is a company engaged in cotton production that faces production efficiency problems due to an ineffective facility layout arrangement. The existing production floor layout does not follow the production process flow, resulting in inefficient material movement, excessive transportation distances, and increased material handling costs. Several departments with close operational relationships are located far apart, causing potential bottlenecks and longer production cycles. This study aims to redesign the production facility layout to minimize material transportation distance and material handling costs using the Systematic Layout Planning (SLP) and Blocplan methods. This empirical study involved direct observation, interviews, material flow analysis, distance calculation, and evaluation of alternative layouts based on transportation distance and Material Handling Cost (OMH). The results indicate that the proposed SLP layout provides the best performance compared with the existing layout and Blocplan alternatives. The material transportation distance was reduced from 139.5 meters to 71.5 meters, representing a 48.74% improvement, while OMH decreased from Rp74,999.92 to Rp39,455.31, resulting in cost savings of Rp35,544.61 or 47.39% efficiency. The proposed layout provides a more effective material flow arrangement and can be recommended as an alternative solution for improving production efficiency at CV. Sidomulyo Perkasa.
Blocplan, Facility Layout, Material Flow, Material Handling Cost, Systematic Layout Planning (SLP).
Evaluation of the Density of Class A Aggregate Foundation Layers Using the Sand Cone Method on the Taba Mulan–Simpang Nangka Road Improvement Project, Bengkulu Province
The Class A aggregate base course is a critical structural component of flexible pavements because its compaction quality directly influences load distribution, structural stability, and pavement service life. This study evaluated the field compaction quality of the Class A aggregate base using the Sand Cone method on the Nakau–South Sumatra Provincial Border Road Preservation Project, specifically the Taba Mulan–Simpang Nangka section, Bengkulu Province. A descriptive quantitative approach was employed using field measurements obtained from ten representative testing locations. Field dry density values determined through the Sand Cone test were compared with the laboratory maximum dry density obtained from the Modified Proctor test (2.23 g/cm³) to calculate the Degree of Compaction (DoC), which was subsequently evaluated against the Bina Marga General Specifications. The measured compaction values ranged from 99.10% to 101.79%, with an average DoC of 100.49%. Eight testing locations (80%) satisfied the minimum requirement of 100% Modified Proctor, whereas two locations (20%) recorded values slightly below the specification. Variations in compaction performance were associated with moisture content, aggregate gradation, layer thickness, roller passes, weather conditions, and construction quality control. The findings confirm that the aggregate base generally complied with national technical standards while highlighting the importance of continuous field quality control to achieve uniform compaction throughout road preservation projects.
Class A Aggregate Base, Degree of Compaction, Modified Proctor, Road Preservation, Sand Cone Method.
Analysis of Traffic Performance on the Sapta Marga Road Section South Curup Subdistrict, Rejang Lebong Regency
Sapta Marga Road is an important urban collector road that connects residential areas, government offices, schools, and commercial centers in South Curup Subdistrict, Rejang Lebong Regency. Increasing community activities have resulted in higher traffic demand, particularly during peak hours, potentially affecting roadway operational performance. This study aimed to analyze traffic flow characteristics, roadway capacity, degree of saturation, and Level of Service (LOS), as well as to formulate traffic engineering recommendations for improving traffic operations. An empirical field survey was conducted over three consecutive days during morning, midday, and afternoon peak periods. Primary data included traffic volume, vehicle speed, roadway geometric characteristics, side-friction conditions, and surrounding land-use activities, while the analysis was performed using the 2023 Indonesian Road Capacity Guidelines (PKJI 2023). The results indicate that Sapta Marga Road currently operates at LOS C–D, suggesting that traffic flow remains stable despite increasing operational pressure during peak periods. Moderate to high side friction caused by on-street parking, roadside commercial activities, pedestrian movements, and vehicles entering and exiting adjacent properties was identified as the primary factor reducing operational efficiency and travel speed. The study recommends improving parking management, traffic signs, pavement markings, pedestrian facilities, and road safety infrastructure to enhance roadway performance and support sustainable urban traffic management.
Degree of Saturation, Level of Service, PKJI 2023, Road Capacity, Traffic Engineering.
The Application of Preventive Maintenance to Improve Heavy Equipment Performance in Construction Projects
Heavy equipment is one of the most critical resources in construction projects, directly influencing project completion in terms of schedule, cost, quality, and operational efficiency. Intensive equipment utilization increases the risk of mechanical failures, making systematic maintenance essential to ensure continuous operation and minimize unexpected disruptions. This study aims to evaluate the implementation of Preventive Maintenance (PM) in improving the operational performance of heavy equipment used in construction projects. An empirical quantitative case-study approach was employed using operational records, maintenance schedules, equipment operating hours, downtime data, and maintenance documentation collected from the Nakau–Taba Mulan Road Landslide Reconstruction Project, Central Bengkulu Regency, Indonesia. Equipment performance was evaluated using key maintenance indicators, including Availability, Reliability, Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), Productivity, and Overall Equipment Effectiveness (OEE). The findings indicate that systematic preventive maintenance significantly reduced equipment downtime while increasing operational availability, reliability, and productivity. The heavy equipment achieved an Availability of 98.90%, a Reliability of 96.00%, and an Overall Equipment Effectiveness (OEE) of 92.03%, indicating world-class operational performance. These results demonstrate that preventive maintenance is an effective maintenance strategy for enhancing equipment reliability, improving operational efficiency, reducing emergency repair costs, and supporting the successful execution of construction projects.
Availability, Downtime, Heavy Equipment, Preventive Maintenance, Productivity
Planning of a Signalized Intersection on the Duren River Road in Jambi Luar Kota District, Muaro Jambi Regency, Jambi Province
Traffic accidents continue to represent a significant transportation safety challenge in Rejang Lebong Regency, particularly along the Curup–Lubuk Linggau and Curup–Kepahiang corridors, where mountainous terrain, sharp curves, steep gradients, and high traffic intensity increase operational risks. This study aims to analyze the characteristics, contributing factors, and spatial distribution of traffic accidents in Rejang Lebong Regency and to identify accident-prone locations for road safety improvement. A quantitative descriptive approach integrated with spatial and statistical analysis was employed using documented accident records, roadway information, and Geographic Information System (GIS)-based mapping. The results indicate that motorcycles are the most frequently involved vehicles, while accidents are predominantly concentrated on national arterial roads with high traffic exposure. Human factors, including excessive speed, unsafe overtaking, lack of concentration, and non-compliance with traffic regulations, were identified as the primary contributors, followed by roadway geometry, vehicle conditions, and environmental influences. Black spot identification using the Equivalent Accident Number (EAN) and Upper Control Limit (UCL) methods successfully identified several high-risk road segments requiring priority engineering interventions. The findings suggest that integrating spatial analysis with engineering assessment provides a reliable framework for supporting evidence-based transportation safety management. Practical recommendations include improving road safety facilities, strengthening traffic law enforcement and public education, and implementing continuous monitoring of accident-prone locations to reduce accident frequency and severity.
Black Spot Identification, Contributing Factors, Rejang Lebong Regency, Road Safety, Traffic Accidents.
Analysis of Traffic Accidents in Rejang Lebong Regency
The Impact of the Self-Help Housing Stimulus Assistance Program (BSPS) on Community Welfare in Selebar District Bengkulu City (A Conceptual Study Based on Literature Review)
The Self-Help Housing Stimulant Assistance Program (BSPS) is a government housing policy initiative designed to transform uninhabitable houses into decent, safe, and healthy residences for low-income communities through self-help mechanisms and community participation. This study aims to analyze the conceptual impact of the BSPS Program on community welfare in Selebar District, Bengkulu City, by employing a literature review-based approach. The research adopts a non-empirical conceptual method through the synthesis of scientific publications, government regulations, statistical reports, and theoretical perspectives related to housing development and welfare improvement. The findings indicate that BSPS contributes positively to community welfare by improving housing quality, residential safety, comfort, environmental health conditions, and beneficiary satisfaction. The analysis further reveals that the effectiveness of BSPS is not solely determined by physical housing improvements but also depends on governance capacity, institutional coordination, community involvement, and the sustainability of post-assistance maintenance. This study emphasizes that BSPS should be positioned as a multidimensional welfare-oriented policy instrument rather than merely a housing rehabilitation program. The conceptual framework developed provides theoretical implications for understanding the relationship between housing intervention and welfare improvement, while offering a foundation for future empirical studies and policy evaluations in community-based housing development.
Community Welfare, Decent Housing, Housing Policy. Self-Help Housing Stimulant Assistance Program (BSPS), Sustainable Development.
The Potential for Applying Lean Construction in Construction Projects in Manokwari Regency: A Systematic Literature Review
The construction industry continues to experience challenges that adversely affect project performance, including schedule delays, resource waste, low productivity, and escalating construction costs. Lean Construction has emerged as a management approach that emphasizes value creation while eliminating non-value-adding activities throughout the construction process. This study aims to evaluate the potential application of Lean Construction in construction projects in Manokwari Regency using a Systematic Literature Review (SLR). The review followed the PRISMA 2020 guideline by systematically identifying, screening, assessing, and synthesizing relevant peer-reviewed publications retrieved from Scopus, ScienceDirect, Google Scholar, Dimensions, and Garuda. The findings indicate that Lean Construction has significant potential to improve labor productivity, reduce material waste, enhance construction quality, shorten project completion time, and increase overall project efficiency. The review further identifies the Last Planner System, Just in Time, Value Stream Mapping, Visual Management, and Continuous Improvement as the most suitable Lean practices for regional construction projects. Nevertheless, successful implementation remains constrained by limited workforce competency, inadequate understanding of Lean principles, logistical challenges, and organizational readiness. The study concludes that Lean Construction offers a promising strategy for improving construction project performance in Manokwari Regency when supported by integrated project management, stakeholder commitment, and continuous capacity development.
Construction Project, Lean Construction, Manokwari Regency, Project Performance, Systematic Literature Review.
Analysis of Cement Fiber Machine Maintenance Using the PMMS (Preventive Maintenance Management System) Method at PT. Nusantara Building Industries
Effective machine maintenance is essential for ensuring production continuity in the fiber cement industry, where high-capacity equipment operates continuously under demanding production conditions. This study analyzes the maintenance performance of cement fiber production machines at PT. Nusantara Building Industries using the Preventive Maintenance Management System (PMMS). An empirical case study approach was employed by collecting data through direct observation, semi-structured interviews, and documentation of maintenance records, PMMS forms, inspection checklists, and downtime reports during the internship period. Machine reliability was evaluated using Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), Pareto analysis, and Fishbone analysis to identify critical equipment and dominant failure causes. The results indicate substantial variations in operational reliability among production machines, with FS6 and FS7 exhibiting the highest downtime and failure frequencies. Pareto analysis revealed that a limited number of failures contributed to most cumulative downtime, while Fishbone analysis identified machine condition, maintenance methods, human factors, environmental conditions, materials, and measurement practices as the principal causes of recurring failures. Reliability-based preventive maintenance scheduling within the PMMS framework established maintenance intervals according to equipment criticality, enabling more effective maintenance planning and resource allocation. The proposed PMMS approach improves maintenance decision-making, enhances equipment reliability, supports production continuity, and provides a practical framework for data-driven maintenance management in continuous manufacturing systems.
Cement Fiber Machine, Downtime, Preventive Maintenance, Preventive Maintenance Management System (PMMS), Reliability.
The Rise of Agentic Artificial Intelligence in Medicine: A Systematic Review of Autonomous Systems, Multi-Agent Collaboration, and Ethical Governance
Artificial Intelligence (AI) in medicine has traditionally functioned as a passive decision-support tool that provides recommendations without autonomous action. The emergence of Agentic AI introduces a new paradigm in which AI systems can plan, execute, and self-correct tasks, creating a shift toward more collaborative human–machine interaction. However, existing literature remains fragmented, with technical development, multi-agent coordination, and ethical governance often discussed separately. This systematic review aims to synthesize current evidence on Agentic AI in medicine by examining autonomous architectures, multi-agent collaboration, clinical validation, ethical challenges, and future implementation barriers. The study followed the PRISMA 2020 framework and employed thematic narrative synthesis of 34 primary studies identified from major academic databases. The findings indicate that orchestrator-based multi-agent architectures integrated with large language models and retrieval-augmented generation represent the dominant approach in medical Agentic AI development. However, real-world clinical validation remains limited, with most studies still focusing on simulations and proof-of-concept evaluations. Furthermore, ethical and governance studies represented the largest research category, highlighting concerns regarding accountability, transparency, and human oversight. This review concludes that the future adoption of Agentic AI requires simultaneous advancement of technical capabilities and governance frameworks, supported by evaluation approaches that consider accuracy, computational efficiency, clinical safety, and human control.
Accountability, Agentic Artificial Intelligence, Ethical Governance, Multi-agent Collaboration, System Autonomy, Systematic Review.
Analysis of the Waste Management System at the Nutritional Support Service Unit (SPPG) to Optimize Kitchen Waste Disposal in the MBG Program (A Conceptual Study Based on Literature Review)
The rapid expansion of Indonesia’s Free Nutritious Meal (MBG) Program has increased the importance of wastewater management systems in Nutritional Support Service Units (SPPGs) as essential infrastructure supporting operational continuity and environmental sustainability. This study aimed to analyze the conformity of existing wastewater management systems in SPPGs with applicable engineering standards and to formulate conceptual strategies for optimizing kitchen wastewater disposal. The study employed a non-empirical conceptual approach based on a structured literature review and comparative engineering analysis of national regulations, technical standards, and previous studies related to building wastewater systems, institutional foodservice facilities, and sustainable waste management. The analysis identified several conceptual gaps between conventional building wastewater design and the operational characteristics of SPPGs, particularly regarding hydraulic capacity, drainage configuration, grease management, maintenance accessibility, and wastewater treatment integration. The findings indicate that although existing engineering standards provide a fundamental basis for wastewater system planning, additional technical adaptations are required to accommodate the unique characteristics of large-scale institutional kitchen wastewater. This study proposes an integrated engineering framework emphasizing production-based hydraulic design, effective pretreatment, preventive maintenance, and sustainable infrastructure planning to improve wastewater system reliability and support the long-term implementation of the MBG Program.
Free Nutritious Meal Program, Kitchen Wastewater, Nutritional Support Service Unit, Wastewater Management System, Wastewater Optimization.
Analysis Factors Causing Delays in Construction Projects for Building Construction in the City of Bengkulu (A Conceptual Study Based on a Literature Review)
Construction project delays are common challenges in building development across Indonesian cities, including Kota Bengkulu, where infrastructure growth in education, government, and commercial sectors continues to increase. This study aims to conceptually identify potential factors causing delays in building construction projects in Kota Bengkulu based on literature findings from previous studies in various Indonesian cities and their relationship with local geographical, climatic, and seismic characteristics. A narrative literature review method was employed by analyzing national and international scientific publications related to construction project delay factors. The results classify delay factors into six aspects: planning and scheduling, project scope and documentation, organization/coordination/communication, resource availability, financial conditions, and external/environmental factors. The characteristics of Kota Bengkulu as a humid tropical region with high rainfall intensity throughout most of the year and an active seismic zone due to tectonic plate interaction along the Indian Ocean coast indicate potential risks, particularly related to environmental conditions and resource availability. This conceptual study provides an initial framework for understanding construction delay mechanisms in Kota Bengkulu; however, empirical research involving actual project data is required to validate the identified factors.
Building Construction Projects, Causal Factors, Construction Delay, Kota Bengkulu, Literature Review.
Analysis of the Relationship Between Cost and Time in the Implementation of Construction Projects (Literature Review)
Cost and time are two fundamental indicators of construction project success alongside quality. These dimensions exhibit a trade-off relationship in which project acceleration generally increases direct costs, whereas schedule delays tend to increase indirect costs and reduce overall project efficiency. This study aimed to examine, through a literature review, the conceptual relationship between cost and time in construction project implementation, the analytical methods commonly applied to evaluate this relationship, and the key factors influencing cost–time performance. The study employed a descriptive narrative literature review based on national and international journal articles addressing construction project cost, project duration, time–cost trade-off, project delays, and schedule acceleration methods. The findings indicate that cost–time relationships are primarily analyzed using the Critical Path Method (CPM), Time–Cost Trade-Off (TCTO), and Earned Value Management (EVM), each supporting different stages of project planning, optimization, and performance monitoring. The review also identifies design changes, material delivery delays, inadequate project management, and inaccurate cost estimation as the dominant factors influencing project cost and schedule performance. The study concludes that integrated cost and time control from project planning through implementation is essential to improve project efficiency and achieve targeted cost and schedule objectives.
Construction Projects, Cost Management, Project Delays, Project Duration, Time–Cost Trade-Off.
Human-Centered Artificial Intelligence in Intelligent Engineering Systems
The increasing integration of artificial intelligence into intelligent engineering systems has created a critical need for approaches that balance computational capability with human autonomy, transparency, accountability, and sustainable operational performance. This study aims to develop an integrative Human-Centered Artificial Intelligence (HCAI) framework capable of supporting trustworthy human–AI collaboration in complex engineering environments. The research employs a non-empirical system-design methodology grounded in design science, systems engineering, socio-technical systems theory, and human-centered AI principles. The proposed framework consists of four interdependent layers comprising human, intelligence, interaction, and governance components that collectively facilitate collaborative decision-making and responsible system operation. Analytical evaluation was conducted through architecture-conformance analysis, requirements-traceability assessment, and scenario-based simulations involving intelligent manufacturing, predictive maintenance, and AI-assisted engineering decision environments. The findings indicate that the framework strengthens human autonomy preservation, explainability capability, collaborative decision efficiency, and governance robustness while maintaining alignment with Industry 5.0 objectives. The study contributes a theoretically integrated architectural model and a reproducible methodological approach for designing intelligent engineering systems that enhance human capabilities, promote trustworthy AI adoption, and support sustainable socio-technical innovation.
Federated Learning for Privacy-Preserving Intelligent Systems
The increasing deployment of intelligent systems across healthcare, industrial automation, smart cities, transportation, and edge-computing environments has intensified concerns regarding privacy, data sovereignty, and secure collaborative learning. This study evaluates the effectiveness of Federated Learning (FL) as a privacy-preserving paradigm capable of supporting distributed intelligence without requiring centralized data collection. An empirical experimental design was implemented using a federated architecture consisting of decentralized client nodes, a central aggregation server, the Federated Averaging algorithm, and integrated secure aggregation with differential privacy mechanisms. Experimental evaluation was conducted through repeated validation under heterogeneous client configurations and varying data distributions. The results demonstrate that the proposed framework achieved strong predictive performance, attaining 93.41% accuracy and 95.28% AUC-ROC while maintaining stable convergence under non-identically distributed data conditions. Security evaluation revealed substantial reductions in model inversion, membership inference, and gradient leakage attacks, confirming the effectiveness of the implemented privacy-preserving mechanisms. Scalability analysis further indicated that the framework maintained reliable performance across expanding client populations with acceptable communication overhead and computational efficiency. The findings confirm that Federated Learning provides a practical and scalable foundation for trustworthy intelligent systems by balancing predictive effectiveness, privacy protection, security resilience, and operational feasibility in distributed environments.
Federated Learning, Privacy Preservation, Intelligent Systems, Distributed Artificial Intelligence, Secure Aggregation.
Edge Intelligence in Smart Manufacturing Ecosystems
The rapid evolution of smart manufacturing ecosystems has intensified the need for intelligent architectures capable of supporting real-time decision-making, operational resilience, and sustainable industrial performance. This study investigates the effectiveness of Edge Intelligence within smart manufacturing environments through an empirical system-design and experimental validation approach. A three-layer architecture consisting of the Industrial Internet of Things layer, the edge intelligence layer, and the cloud orchestration layer was developed and evaluated under predictive maintenance, production scheduling, and anomaly detection scenarios. Performance assessment employed metrics including inference latency, response time, bandwidth consumption, prediction accuracy, throughput, resource utilization, reliability, resilience, and energy efficiency. The experimental results demonstrate that edge-enabled intelligence significantly improves manufacturing performance by reducing latency and communication overhead while increasing operational responsiveness, decision consistency, throughput, and system reliability. The architecture also enhances adaptive decision-making capabilities, strengthens human-machine collaboration, improves cybersecurity resilience, and contributes to environmental sustainability through more efficient resource utilization and reduced carbon emissions. The findings establish Edge Intelligence as a strategic ecosystem capability that enables resilient, adaptive, human-centric, and sustainable manufacturing systems aligned with the emerging objectives of Industry 5.0.
Edge Intelligence, Smart Manufacturing, Industrial Internet Of Things, Industry 5.0, Manufacturing Ecosystems.
Digital Twin Technology for Sustainable Industrial Operations
Digital Twin technology has emerged as a strategic enabler for sustainable industrial transformation by integrating physical operations, virtual representations, predictive analytics, and sustainability-oriented decision support into a unified cyber–physical environment. This study aims to develop and analytically evaluate a comprehensive Digital Twin framework capable of supporting sustainable industrial operations through the integration of operational efficiency, energy performance, resource optimization, and system resilience dimensions. A non-empirical system design approach was employed to construct a multilayer architecture consisting of physical operation, data acquisition, communication and synchronization, digital twin modeling, analytics and optimization, and sustainability decision-support layers. Technical evaluation was conducted through model-based simulation and analytical assessment using standardized sustainability and operational indicators. The findings demonstrate that the proposed framework strengthens operational visibility, predictive maintenance capability, energy efficiency, resource utilization, responsiveness, and resilience through continuous interaction between physical and virtual environments. The analysis further indicates that Digital Twin integration facilitates circularity, sustainability governance, and Industry 5.0 readiness by enabling adaptive and data-driven industrial decision making. The study contributes a holistic conceptual framework that advances the understanding of Digital Twin technology as a sustainability-enabling infrastructure for future industrial systems.
Digital Twin, Sustainable Operations, Industry 5.0, Industrial Sustainability, Cyber-Physical Systems.
Explainable Artificial Intelligence for Engineering Decision Support Systems
Explainable Artificial Intelligence (XAI) has become increasingly important in Engineering Decision Support Systems because growing algorithmic complexity often reduces transparency, accountability, and practitioner confidence despite substantial improvements in predictive capability. This study develops a conceptual framework that integrates engineering data processing, artificial intelligence inference, explainability mechanisms, and human-centered decision support within a unified multilayer architecture. A non-empirical system design methodology based on design-oriented systems research, architecture-based evaluation, and scenario-driven analytical simulation is employed to examine architectural consistency, interoperability, traceability, and explainability across representative engineering contexts. The analytical results indicate that explanation fidelity, interpretability, transparency, traceability, and modular scalability function as complementary engineering quality attributes that collectively strengthen trustworthy decision support while preserving logical consistency between engineering evidence and computational reasoning. The proposed architecture also demonstrates technology independence and adaptability across heterogeneous engineering domains through explicit modular interactions and standardized information flows. This study contributes an architecture-centered perspective that advances theoretical understanding of explainable engineering intelligence while providing a reproducible conceptual foundation for future empirical implementation, quantitative validation, and standardized evaluation of trustworthy Engineering Decision Support Systems.
Explainable Artificial Intelligence, Engineering Decision Support Systems, Human-Centered Intelligence, Transparent Decision Making, System Architecture
Human Activity Recognition Using Deep Learning Models on the UCI HAR Dataset
Human Activity Recognition (HAR) has emerged as a critical component of wearable sensing and intelligent healthcare systems, necessitating robust and computationally efficient deep learning architectures. This study presents an empirical experimental evaluation of three deep learning models 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM using the UCI HAR dataset under a subject-independent protocol. Raw inertial signals were segmented, normalized, and processed through standardized preprocessing pipelines to ensure reproducibility. Performance was assessed using accuracy, precision, recall, F1-score, cross-validation stability, and computational efficiency metrics. Results indicate that the CNN–LSTM architecture achieves the highest test accuracy (94.87%) and demonstrates improved robustness with lower variance and reduced sensitivity to signal perturbations compared to standalone models. Computational analysis confirms that the hybrid configuration maintains feasible inference latency for real-time applications despite moderate increases in parameter size. The findings validate the effectiveness of integrated spatiotemporal feature learning and provide a reproducible benchmark for future research on deep learning–based HAR systems in wearable and IoT contexts.
Human Activity Recognition, Deep Learning, CNN-LSTM, UCI HAR Dataset, Wearable Sensors.
Credit Card Fraud Detection Using Random Forest and XGBoost on a Public Kaggle Dataset
Credit card fraud detection remains a critical challenge in digital financial ecosystems characterized by extreme class imbalance and evolving attack patterns. This study adopts an empirical experimental design to evaluate and compare Random Forest and XGBoost models using the publicly available Kaggle Credit Card Fraud Detection dataset. A controlled pipeline incorporating stratified data splitting, SMOTE-based imbalance mitigation, and GridSearchCV hyperparameter optimization was implemented to ensure methodological consistency and reproducibility. Performance was assessed through precision, recall, F1-score, ROC-AUC, confusion matrix analysis, and computational efficiency metrics. Results indicate that XGBoost outperformed Random Forest in recall, F1-score, and ROC-AUC, demonstrating enhanced minority-class discrimination and reduced false negatives under optimized conditions. Random Forest exhibited competitive precision and interpretability transparency, though with slightly lower sensitivity. Scalability evaluation confirmed that both models maintained low inference latency suitable for near-real-time deployment. The findings highlight the critical role of imbalance handling and parameter optimization in ensemble-based fraud detection and support boosting-oriented approaches as strategically advantageous for operational financial security systems.
Credit Card Fraud, Random Forest, Xgboost, Class Imbalance, Ensemble Learning
Air Quality Index Prediction Using Machine Learning Algorithms on the Beijing PM2.5 Dataset
Accurate prediction of Air Quality Index (AQI) is critical for mitigating public health risks associated with urban air pollution. This study presents an empirical analysis of PM2.5 concentration forecasting in Beijing using advanced machine learning algorithms, integrating high-resolution atmospheric data and meteorological variables. A multi-stage pipeline was implemented, including data preprocessing, feature selection, and model training with Random Forest, Gradient Boosting, Support Vector Regression, and Long Short-Term Memory (LSTM) networks. Predictive performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R², while spatial and temporal fidelity was assessed across districts, diurnal cycles, and seasonal periods. LSTM models consistently achieved superior accuracy, capturing short-term pollution spikes, seasonal variability, and spatial heterogeneity, whereas ensemble methods provided stable baseline predictions with moderate sensitivity to extreme events. Sensitivity analysis identified wind speed, humidity, and neighboring PM2.5 measurements as key predictors. The results demonstrate that integrating recurrent neural networks with ensemble approaches enables reliable, operationally relevant AQI forecasts, offering both theoretical validation of sequential modeling for urban air quality and practical guidance for environmental monitoring, public health interventions, and city-level policy implementation.
PM2.5, Air Quality Index, Machine Learning, LSTM, Beijing.
Network Intrusion Detection Using Ensemble Learning Techniques on the CIC-IDS2017 Public Dataset
Network intrusion detection remains a critical challenge in cybersecurity due to evolving attack patterns, class imbalance, and high-dimensional network traffic data. This study investigates the effectiveness of ensemble learning techniques on the CIC-IDS2017 public dataset, integrating decision trees, random forests, and gradient boosting models through stacking, voting, and hybrid Boost-Bag strategies. Data preprocessing involved normalization, handling missing values, and feature selection based on correlation and mutual information to reduce dimensionality while preserving predictive relevance. Empirical evaluation employed stratified 10-fold cross-validation and performance metrics including accuracy, recall, F1-score, and AUC-ROC, with additional analyses of confusion matrices and temporal stability to assess operational reliability. Results indicate that hybrid ensembles achieve superior detection performance, particularly for low-frequency attacks, while maintaining moderate computational overhead compared to individual classifiers. Comparative insights reveal trade-offs between accuracy, minority-class sensitivity, and inference latency, guiding practical deployment considerations. The findings substantiate the theoretical benefits of ensemble diversity and optimized feature selection, offering a robust framework for scalable, interpretable, and resilient network intrusion detection systems.
Ensemble Learning, Network Intrusion Detection, CIC-IDS2017, Feature Selection, Hybrid Models.
Short-Term Electricity Load Forecasting Using Hybrid CNN–LSTM Models on the UCI Electricity Load Dataset
Accurate short-term electricity load forecasting (STLF) is critical for efficient energy management, demand response, and grid stability in modern residential environments. This study presents an empirical investigation of a hybrid convolutional neural network–long short-term memory (CNN–LSTM) model applied to the UCI Electricity Load dataset, integrating convolutional layers to extract localized temporal features and stacked LSTM layers to model long-term dependencies across households. The model is trained using Python and TensorFlow on a GPU-enabled workstation, with preprocessing including normalization, sliding-window sequence generation, and train-validation-test splitting. Performance is evaluated through mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and k-fold cross-validation. Comparative benchmarking against LSTM, CNN-GRU, and multi-scale CNN-LSTM architectures demonstrates superior accuracy, stability, and generalizability of the proposed hybrid model. Sensitivity and interpretability analyses further reveal critical temporal patterns, feature contributions, and operational insights, facilitating actionable energy management decisions. These results substantiate the hybrid CNN–LSTM approach as a robust, interpretable, and operationally relevant solution for STLF applications.
Short-Term Load, CNN–LSTM, Hybrid Forecasting, Electricity Prediction, Energy Management.