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Journal of ICT, Design, Engineering and Technological Science
Published by Universitas Muhammadiyah Purwokerto
ISSN : -     EISSN : 26042673     DOI : https://doi.org/10.33150/JITDETS-8.1.1
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering
Journal of ICT, Design, Engineering and Technological Science (JITDETS) focuses on the logical ramifications of advances in information and communications technology. It is expected for all sorts of experts, be it scientists, academicians, industry, government or strategy producers. It, along these lines, gives an exceptional discussion to papers covering application-based research subjects significant to assembling procedures, machines, and process reconciliation. JITDETS maintains the high standard of excellence of publishing. This is guaranteed by subjecting each paper to a strict evaluation strategy by individuals from the universal publication counseling board. The goal is solid to set up that papers submitted do meet all the requirements, particularly with regards to demonstrated application-based research work. It is not satisfactory that papers have a hypothetical substance alone; papers must exhibit producing applications.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 82 Documents
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Utilizing a Hybrid Deep Learning Architecture For Salat Posture Detection Abdul Salam Shah; Farhan Akbar; Muhammad Adnan Kaim Khani; Adil Maqsood; Fahad Shah Bukhari
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.1.3

Abstract

A lot of Muslims have trouble getting their daily prayers right. You know, Salat with the movements and the recitations. It disrupts their religious duties. They do not get quick feedback on how their form looks. So we put together this system. It grabs images right as they happen. Then it checks them out using a convolutional neural network. That is CNN for short. It spots and confirms the basic postures in Salat. The thing covers six main positions. Takbir. Qiyam. Ruku. Sujood. Tashahhud. And Salam. Pretty much opens it up for tons of people to use. We tested how well it works. Looked at pose detection accuracy. Response time, too. And what users thought about it. Turns out the system helps a bunch. Folks can improve their Salat quality with it. Shows how computer vision and deep learning fit into something like this. Not your usual setup.
Understanding How Project Complexity and Interoperability Standards Inϐluence the Effectiveness of Smart Construction Technologies Josh Chew
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.1.4

Abstract

The growing use of smart construction technologies has made construction projects evolve into data‑driven engineering frame‑ works. Still, the level at which the technologies can optimize project integration performance is subject to the preconditions of the underlying systems. This paper formulates a system‑based computational model to analyze how the capability of smart construction technology can influence the project integration performance under the moderating influence of the project complexity and data interoperability standards. Based on the data obtained through the work with 233 professionals who dealt with digitally enabled construction projects in Singapore, the study conceptualizes construction projects as socio‑technical systems where digital technologies can be seen as subsystems of performance enhancement under different environmental limitations. They used a MATLAB‑based analytical framework to estimate the parameters of the system, the effect of interaction, and the predictive accuracy. The findings show that the capability of smart construction technology has a strong positive impact on project integration performance through enhancing the consistency of design‑data, cost alignment, and schedule coordination. This relationship was revealed to be undermined by project complexity due to the creation of system‑level disturbances, and data interoperability standards were found to be a strong force that could modify the effect of tech capability by improving the level of subsystem integration and the coherence of information. The high explanatory and predictive capability of the proposed model has validated that it is a strong model to use in evaluating engineering performance. This study contributes to the socio‑technical systems theory and offers quantitative data that the optimal integration performance is achieved when the deployment of digital capability is coordinated with good complexity management and a strong interoperability architecture. The results provide practical information to engineers, system designers, and policymakers who are interested in optimizing digitally integrated construction systems.
Machine Learning–Based Prediction and Interpretability Analysis of Ultra‑High‑Performance Concrete Compressive Strength Using Random Forest Imran Ali Channa; Muhammad Khisrow Khan; Saad Hanif; Abdul Wahab
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.1.5

Abstract

Ultra‑High‑Performance Concrete (UHPC) is a considerably advanced cementitious concrete with great characteristics of strength and durability, but the compressive strength is highly dependent on the multi‑faceted interplay between mixture proportions and curing conditions. These interactions are nonlinear and multivariate, making it difficult to accurately estimate the UHPC compressive strength using the previous experimental and empirical methods. In the paper, a Random Forest (RF) regression model has been constructed to estimate UHPC compressive strength based on a large‑scale dataset of 810 samples and 13 predictors (material composition and curing parameters). Multiple statistical measures were strictly used to evaluate the performance of the model, such as R2, RMSE, MAE, MAPE, and CVRMSE, as well as 10‑fold cross‑validation to evaluate stability and ability to generalize. The optimized RF model had a high predictive accuracy with a value of 0.96 on the testing set and small values of errors, which showed high robustness and consistency in diverse segmentations of data. Hyperparameter tuning also improved the model performance by finding a balance between model complexity and generalization. SHAP (Shapley Additive Explanations) analysis was used to enhance the transparency and interpretability of the models, to measure the contribution of the individual input feature to the compressive strength predictions. The findings demonstrated that curing age, fibre, silica fume, and dosage of superplasticizer were the most significant parameters that controlled the strength development of UHPC. The suggested modeling framework reveals the efficiency of bringing ensemble machine learning along with explainable artificial intelligence methods to provide accurate, reliable, and interpretable predictions of UHPC compressive strength, which creates a useful instrument in the process of mix design optimization and performance evaluation.
A Comprehensive Geotechnical Evaluation of Subsoil Engineering Properties Including Index, Compaction, Shear Strength, and Compressibility Characteristics for Foundation Design and Overall Construction Suitability Assessment Yaser Farman; Saad Hanif; Syed Zamin Raza Naqvi; Muazzam Nawaz; Muhammad Naveed Khalil
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.2.1

Abstract

The project provides a comprehensive geotechnical evaluation of the geotechnical characteristics of the underground engineering that is relevant to the foundation design and constructability assessment. Edafic samples were sampled at multiple locations and underwent controlled laboratory tests characterized to outline index parameters, compaction behaviour, shear strength coefficients, compressibility traits, consolidation reactions, settlement tendencies, as well as hydraulic permeabilities. The index testing revealed that the soils are mostly under the CH, CL, CI, and NP categories of the Unified Soil Classification System, indicating the large proportion of highly plastic clays, low to intermediate plasticity clays, and non‑plastic granular assemblages. Compaction tests produced the best moisture levels between about 6% and 20% and the highest dry densities of between 1777 kg/m3 and 2341 kg/m3. Parameters of shear strength indicated cohesion values to 111 kPa, and friction angles of 49 o, thus indicating heterogeneous bearing‑capacity regimes. The compression indices of consolidation tests (0.035‑0.070) and settlement projections were moderate, with an overall settlement that falls within the acceptable limits of shallow foundations. Determinations of permeability emphasized a high degree of variability, and in correspondence with the range of grain‑size distribution. Overall, the findings highlight the existence of a heterogeneous subsurface, whose strength and compressibility are moderate, which requires site‑specific foundation plans to maintain the structural integrity and assure the sustainability of the performance in the long term.
Weather Forecasting Using LSTM Neural Networks, Temperature Prediction Seyed Ebrahim Hosseini; Shahbaz Pervez; Kazim Raza Talpur; Omar Alhawi; Abdul Salam Shah; Asadullah Shah
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.2.2

Abstract

The study explores the field of weather prediction with attention to its real‑world use and the quality that can be enhanced with the help of the most advanced methods of machine learning. It particularly studies the application of long short‑ term memory (LSTM) networks to improve forecasting. The paper relies on the data collected in Delhi, India, to train and evaluate the LSTM model. The paper identifies critical gaps in weather forecasting studies and investigates the effects of the gaps on business and lives. Accurate weather prediction can be used in such industries as agriculture, transport and disaster management, where a small development may have tremendous consequences. The paper provides a clear overview of LSTM model, the architecture, validation policy and evaluation. LSTM networks are the most appropriate networks to make the weather forecasts given that they are capable of repeating the patterns using the time series data. The paper will reveal that LSTM networks are efficient to improve the accuracy of weather prediction as well as how they may revolutionize industries that are reliant on the accuracy of predictions. This model was trained in this study based on an effective evaluation of MSE 0.034.
Contractor Capability and Sustainable Project Outcomes: The Mediating Role of Life‑Cycle Cost Awareness and the Moderating Role of Regulatory Support Muhammad Hamid Murtza
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.1.6

Abstract

This study investigates the influence of contractor capability on project performance and environmental sustainability, while ex‑amining the mediating role of life‑cycle cost awareness and the moderating effect of regulator support within Australia’s commercial and public building sectors. A quantitative cross‑sectional survey was conducted among construction professionals involved in commercial and public building projects. Using 185 valid responses, data were analyzed in STATA through reliability and validity assessment, correlation analysis, and structural modeling to test direct, mediating, and moderating relationships. The results indicate that contractor capability has a significant positive impact on both project performance (β = 0.611, p < 0.001) and environmental sustainability (β = 0.728, p < 0.001). Contractor capability also significantly predicts life‑cycle cost awareness (β = 0.693, p < 0.001), which in turn enhances project performance (β = 0.314, p < 0.001) and environmental sustainability (β = 0.341, p < 0.001). Regulator support strengthens life‑cycle cost awareness directly (β = 0.214, p = 0.001) and significantly moderates the relationship between contractor capability and life‑cycle cost awareness (β = 0.196, p = 0.006). Model fit indices demonstrate satisfactory fit (CFI = 0.947; RMSEA = 0.054). This study advances sustainable construction theory by integrating capability‑based and institutional perspectives and highlights life‑cycle cost awareness as a critical mechanism through which contractor capability translates into dual performance–sustainability outcomes. The findings provide actionable guidance for contractors and policymakers to improve project success and environmental outcomes through capability development and stronger regulatory support.
Deep Learning Architectures for Concrete Compressive Strength Prediction: A State‑of‑the‑Art Review of CNN, ANN, and Hybrid Models M. Adil Khan; Imran Ali Channa; Saad Hanif; Baitullah Khan Kibzai
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.1.7

Abstract

Structural safety, optimization of materials, and sustainable construction practice depend on the prediction of concrete compressive strength. Traditional methods of testing use the laboratory method, which is time‑consuming, expensive, and destructive. Recent progress in deep learning has made it possible to predict the compressive strength in an accurate, rapid, and non‑destructive way by modeling nonlinear complex relationships between the constituents of concrete, curing conditions, and mechanical performance. This review is a systematic review of the deep learning architectures that have been applied to predict the concrete compressive strength with state‑of‑the‑art, such as Convolutional Neural Networks (CNNs), Artiϐicial Neural Networks (ANNs), Deep Neural Networks (DNNs), Long Short‑Term Memory (LSTM) networks, Gated Recurrent Units (GRU), Transformer‑based models, and hybrid architectures (CNN‑LSTM, CNN‑GRU, and ensemble stacking). It has been shown in the literature that higher hybrid and ensemble models allow the high predictive performance to be achieved, with the value of R² often exceeding 0.95, with the best possible models having an R² of 0.99 when using controlled datasets. Both metaheuristic optimization algorithms (e.g., PSO, GA, ACO, TLBO) and Bayesian hyperparameter tuning would greatly increase the model generalization and robustness. Moreover, interpretable artificial intelligence tools, such as SHAP and sensitivity analysis, have enhanced interpretability, and cement content, curing age, and water‑cement ratio are confirmed to be the most significant predictors of strength. Applications have been spread over the spe‑ cialized materials like ultra‑high‑performance concrete (UHPC), geopolymer concrete, recycled aggregate concrete, self‑compacting concrete, and waste‑based sustainable concretes. However, the issues of data standardization, cross‑laboratory generalization, and model transparency persist in spite of impressive advances. The future research is to be directed at physics‑informed neural networks, the multi‑objective optimization that considers the metrics of environmental impact, real‑time edge deployment, and the standardized benchmark datasets. In general, methods using deep learning as its core technology can be discussed as a revolutionary development in intelligent concrete design and sustainable construction engineering
BERT‑LSTM‑LGBM Approach for DDoS Attacks Detection in IoT Network Using ML Imdad Ali Shah; Noor Zaman Jhanjhi
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS‑9.2.3

Abstract

New cybersecurity challenges have increased as the interconnected IoT devices grow, such as DDoS attacks, which are observed as more attacks exploit resource‑constrained IoT devices. Conventional detection mechanisms often fail to capture the dynamic and diverse nature of IoT network traffic, and several researchers and professionals have addressed these concerns. In view of the issues raised by the researchers, the presented models need to enhance their accuracy and performance. The BERT_LSTM‑LGBM model has been proposed for an intelligent and accurate DDoS attack detection in IoT devices. BERT component is used to remove deep contextual features from network traffic data, capturing intractable relationships and semantic dependency. The long Short‑Term Memory (LSTM) network further improves temporal arrangements learning to detect sequential anomalies, while the LGBM classifier promises high‑speed and comprehensible decision‑making. The results show that the BERT‑LSTM‑LGBM framework is robust and can detect diverse DDoS attack patterns, offering a scalable and intelligent solution for securing next‑generation IoT infrastructures. Our proposed model presents its exceptional proficiency in threat detection within the IoT environment. We achieved remarkable results such as 99.8%, 98%, and 99%.
Sustainable Concrete Mixture Design for Reducing Embodied CO₂: A Comprehensive Data‑Driven Assessment of Material Composition, Environmental Indicators, and Predictive Modeling for Low‑Carbon Construction Applications M. Adil Khan; Asad Ullah Khan; Saad Hanif; Syed Zamin Raza Naqvi
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.2.4

Abstract

Minimising embodied CO2 in concrete is one of the most important tasks that needs to be performed in order to attain a sustain‑able construction and prevent the effects of the changes in climatic conditions. This paper is a comparative analysis of three machine learning models: Linear Regression (LR), AdaBoost (ADB), and K‑Nearest Neighbours (KNN) to predict embodied_CO2 based on a dataset of 1,000 ob‑servations in the form of mixture composition, material properties, and environmental indicators. The descriptive statistical analysis assured the balanced distribution of most variables with little skewness, whereas the correlation analysis revealed cement and resource consumption as the leading factors contributing to embodied_CO2. Training, testing, split, and k‑fold cross‑validation based on the R, MAE, RMSE, RAE, and RRSE metrics were used to measure the model performance. Findings reveal that KNN was a better method in comparison with LR and ADB in all assessment systems. KNN with k‑fold validation had a correlation coefficient of 0.9996, MAE of 1.8668, and RMSE of 2.5041 versus LR (R = 0.9874, MAE = 11.3218, RMSE = 13.0931) and ADB (R = 0.9764, MAE = 14.5647, RMSE = 18.0974). The same tendencies were noted in the testing stage, with KNN having R = 0.9996, MAE = 1.9273, and RMSE = 2.7044, which are considerably lower than LR (MAE = 11.0947; RMSE = 12.8293) and ADB (MAE = 13.9921; RMSE = 16.8487). The residual analysis also indicated that KNN has better stability, with tightly clustered and symmetric error distributions and a small generalisation gap. The results show that instance‑based learning is effective to learn complex nonlinear associations in embodied carbon prediction. This paper emphasizes the significance of strong cross‑validation and residual diagnos‑tics in model selection and shows the feasibility of machine learning in aiding the design of low‑carbon concrete with regard to design strategies.
A Hybrid NLP and Deep Learning Framework for Phishing Detection in Emails and URLs Basheer Riskhan; Md Saiful Arefin; Mutasim Billah; Abdullah Al Hadi; Siti Shafrah Shahawai; Siva Raja Sindiramutty; Noor Zaman Jhanjhi
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.2.5

Abstract

Phishing attacks are constantly evolving, exploiting users with malicious URLs and misleading emails, while conventional rule‑based detection methods struggle to keep pace with new threats. To improve detection accuracy and adaptability, this study proposes a hybrid phishing detection framework that combines Deep Learning (DL) and Natural Language Processing (NLP) techniques. For email classification, the system uses TF‑IDF‑based feature extraction, including word‑ and character‑level n‑grams, domain encoding, and link‑count analysis; for URL analysis, character‑level tokenisation and manually created structural features are used. In addition to CNN, LSTM, and Hybrid CNN‑LSTM models for URL classification, three deep learning architectures are developed for email detection: Convolutional Neural Network (CNN), Bidi‑rectional Long Short‑Term Memory (BiLSTM), and a Hybrid CNN‑BiLSTM model. The hybrid architectures efficiently capture intricate phishing patterns by combining sequential dependency learning with spatial feature extraction. Both primary email and large‑scale URL datasets are used, with stratified data partitioning and suitable preprocessing methods, to assess the proposed framework. The methodology addresses the drawbacks of static, single‑model systems in contemporary cybersecurity environments by demonstrating a scalable, flexible approach to phishing detection.

Page 8 of 9 | Total Record : 82

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