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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 7 Documents
 1 
Search results for , issue "Volume 9, Issue 1" : 7 Documents clear
Evaluating Supervised Machine Learning Algorithms for Cybersecurity Threat Detection Using the CICIDS 2023 Dataset Ahmed Alwan; Asadullah Shah; Alwan Abdullah Abdulrahman Alwan; Shams Ul Arfeen Laghari
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-9.1.1

Abstract

With the increasing volume and sophistication of network threats in IoT environments, real-time intrusion detection has become essential for securing cyber-physical systems. This study investigates the use of supervised machine learning algorithms to detect network intrusions using the CICIDS 2023 dataset. Five classification models—Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and k-Nearest Neighbors—were evaluated for their effectiveness in both binary and multi-class classification tasks. The study incorporates feature selection, dimensionality reduction, and a deployment-oriented performance metric called Real-Time Suitability Score (RTSS) to assess the trade-off between accuracy, inference speed, and model size. The experimental results highlight the potential of lightweight models for deployment in constrained environments and demonstrate the impact of feature importance and classification performance on real-time detection. The findings contribute to the design of efficient and explainable AI-based intrusion detection systems, and recommendations for future work include improving model interpretability and expanding evaluation to more diverse threat categories.
Student Academic Performance Prediction using Ensemble Learning Methods Muhammad Abdul Rehman; Asim Iftikhar; Saghir Muhammad; Rizwan Ahmed
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-9.1.2

Abstract

The evaluation of students’ academic performance is a fundamental aspect of any educational institution, playing a critical role in shaping students’ academic journeys and institutional decision‑making. However, this process presents signi icant challenges, particularly when dealing with large student populations. Traditional methods of result evaluation often lead to inef iciencies, delays in processing, and increased workload for institutions. With the rapid advancements in information technology and arti icial intelligence, automated systems have revolutionized student performance assessment,making the process faster,more accurate, and less labor‑intensive. Machine learning has emerged as a powerful tool in this domain, enabling the prediction of student performance through techniques such as regression and classi ication. While these models provide valuable insights, their effectiveness largely depends on accuracy. Achieving high accuracy in grade prediction remains a signi icant challenge, as even slight inaccuracies can lead to misclassi ication, affecting students’ academic outcomes. To overcome these limitations, ensemble learning methods have proven to be highly effective. These techniques combine multiple models to enhance predictive performance and reduce errors. This study focuses on evaluating various ensemble methods, including random forest, bagging, boosting, and extreme gradient boosting, to determine the most reliable approach for predicting student performance. A comparative analysis was conducted to assess the accuracy and ef iciency of these models using key evaluation metrics. The results indicate that extreme gradient boosting out performed other models, achieving the highest accuracy in predicting student grades. This research highlights the importance of ensemble learning in academic performance assessment andunderscoresits potential to improve decision‑making in educational institutions.
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.
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

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