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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 6 Documents
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Search results for , issue "Volume 9, Issue 2" : 6 Documents clear
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.
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.
Technology Readiness and Safety Outcomes in Construction: The Mediating Role of Worker Competence and Moderating Role of Top Management Support Ishtiaq Ahmad
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‑9.2.6

Abstract

Construction projects remain highly vulnerable to accidents due to dynamic workflows, hazardous environments, and limitations of conventional safety management approaches. With the growing adoption of Industry 4.0 technologies, construction organizations are in‑creasingly investing in digital safety tools; however, their effectiveness depends on organizational readiness and workforce capability to im‑plement them. Grounded in Socio‑Technical Systems (STS) Theory, this study examines the impact of Technology Readiness (TR) on Safety Climate (SC) and Safety Performance (SP), while assessing the mediating role of Worker Competence (WC)and the moderating influence of Top Management Support (TMS). A quantitative cross‑sectional survey was conducted using responses from 420 construction professionals drawn from both public (n=200) and private (n=220) sector organizations. An engineering‑oriented predictive modeling approach was applied, and the model demonstrated strong predictive performance, explaining 62% of the variance in safety climate (R²=0.62) and 58% in safety perfor‑mance (R²=0.58) with acceptable prediction error (SC: RMSE=0.41, MAE=0.32; SP: RMSE=0.45, MAE=0.35). Scenario analysis indicated that high technology readiness substantially improves predicted SC and SP, while competence improvement and strong management support generate similarly large gains in safety outcomes. Sensitivity analysis identified worker competence as the most influential predictor for both SC and SP,followed by technology readiness and top management support. Further, the sector‑wise comparison revealed that private sector organizations demonstrated a stronger link between technology readiness and increases in worker competence, as well as greater improvements in safety out‑comes associated with readiness, compared to public sector organizations. This suggests that private sector organizations were more effective at converting digital investments into competence and safety gains, possibly due to fewer institutional barriers or different organizational struc‑tures. The study concludes that sustainable safety improvement requires integrated strategies that enhance technology readiness, strengthen workforce competence, and reinforce leadership support to maximize the operational safety value of digital transformation in construction or‑ganizations.

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