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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 87 Documents
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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.
A Custom CNN Architecture for Image Recognition using CIFAR‑10 Hayden Bin Nor Azman; Abdul Salam Shah
Journal of ICT, Design, Engineering and Technological Science Volume 8, 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-8.2.6

Abstract

Deep Learning algorithms have remained prominent in image classification across various domains. CNNs have modernized the process, enabling automated classification in new fields. Traditional models suffer from lower accuracy largely due to manual feature extraction. To address challenges in modern algorithms, this paper proposes a CNN‑based model for the multi‑class problem using the CIFAR‑10 dataset. The hyperparameters have been carefully selected to achieve higher accuracy while avoiding overfitting. Data augmentation and dropout layers contributed to achieving 85.49% accuracy
Intelligent Survivor Detection System for Post-Earthquake Rescue Operations Using IoT and Sensor-Based Monitoring Sarosh Aziz; Shahbaz Ali Khan; Abdul Salam Shah; Muhammad Adnan Kaim Khani; Adil Maqsood; Asadullah Shah
Journal of ICT, Design, Engineering and Technological Science Volume 10, 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-10.1.2

Abstract

The paper introduces a system to enhance living human detection in earthquake disaster scenarios. The focus is on developing an overall solution to quickly identify and locate each person trapped within damaged structures or in debris. The suggested system incorporates a Passive Infrared (PIR) sensor, an Ultrasonic sensor, and a Microwave sensor into a small gadget that can be mounted to a telescopic selfie stick to reach confined areas. The main goals will be to develop a device capable of real-time human presence detection, to provide a visual output as feedback to help rescue teams respond efficiently, and to include an alarm system that alerts rescue teams when trapped human beings are detected. The system’s methodology would use PIR sensors to detect heat and movement, Ultrasonic sensors to reflect sound waves, and Microwave technology to detect movement and identify any living human being behind the walls. An Arduino UNO microcontroller is used for data processing and control to ensure the system is practical. The sensors are strategically incorporated into a portable device for easy deployment at disaster-stricken locations. In general, the system will play an important role in enhancing response to earthquake disasters. The expected outcomes are the successful creation of a working prototype, faster response times to save trapped people, and an understanding of the limitations of the sensors in earthquake scenarios. The article describes the new application of technology to address severe problems in the event of an earthquake or other natural disaster, helping save human lives and enhancing disaster management programs.
Design And Development of a Nationwide Centralized Blood Bank System for Efficient Blood Donation and Distribution Basheer Riskhan; Abdullahi Mohamed Ali; Ibrahim Osman Sheikh Hussein; Abdirahman Ibrahim Osman; Siva Raja Sindiramutty; Noor Zaman Jhanjhi
Journal of ICT, Design, Engineering and Technological Science Volume 10, 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-10.1.3

Abstract

A centralized digital system, the Nationwide Centralized Blood Bank System (NCBBS), is proposed to address operational inefficiencies in blood donation and distribution in Sri Lanka, driven by decentralization and semi-manual processes. Although a well-founded National Blood Transfusion Service (NBTS) and a well-established voluntary donor culture are in place, a lack of real-time nationwide visibility into blood stocks leads to shortages, wastage, and delays in emergency situations. This report describes the design and development of a centralized, web-based application that integrates donor registration, health screening, laboratory testing, blood unit management, request processing, and emergency prioritization into a single, secure system. The system uses an Agile System Development Life Cycle (SDLC) and a layered multi-tier architecture comprising presentation, business logic, data management, and security layers. A role-based access control model with eight user roles implements accountability for operations and data in accordance with the Personal Data Protection Act in Sri Lanka. Modular system design helps in scalability, maintainability, and integration of predictive analytics and decision support in the future. Nationwide blood bank management through consolidation of stakeholders, such as blood collectors, blood storage facilities, requester organizations, and administrators into a single coordinated ecosystem, NCBBS promotes transparency, enhances inter-institutional coordination, and creates a safe platform on which nationwide blood bank management can be realized in accordance with international best practices of digital health.
Hybrid Machine Learning-based Short-Term Electricity Price Forecasting in Smart Grids Using Weather, Demand, and Market Data: A Systemic Review Asad Riaz
Journal of ICT, Design, Engineering and Technological Science Volume 10, 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-10.1.4

Abstract

Electricity Price Forecasting (EPF) represents one of the most important areas of activities of smart grid, as it directly affects the economic dispatch, demand responses, risk hedging, and reliability of the systems. Short term EPF is a difficult engineering problem due to the nonlinearity, volatility, and sensitivity to exogenous variables like weather and demand. This article provides a thoroughly structured and comprehensive analysis of hybrid Machine Learning (ML)-based methods of solving problems in short-term EPF and carries out the review of the literature published since 2015 and until 2025. Under the engineering view, the concept of review conceptualizes EPF as a data-driven forecasting pipeline which is composed of data acquisition, preprocessing, feature extraction, model training, and forecasting output. The paper studies the topic of hybrid architectures which combine signal decomposition methods (e.g., EMD, VMD, wavelets), sophisticated learning methods (e.g., CNN-LSTM, attention mechanisms, transformers), and ensemble policies including stacking and quantile regression averaging. These hybrid systems are evaluated on how well they can model temporal dependencies, the cross-feature interactions as well as extreme price behaviors. The review also speaks of formal evaluation practice based on conventional performance measures, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) and probabilistic forecasting measures. The analysis of comparable results of benchmark datasets and real-world electricity markets indicates hybrid models tend to perform better than individual learners in both point and probabilistic forecasting tasks. But the difference in performances between market structures, forecasting horizons (inter and day ahead), and regime conditions through the use of strong validation procedures including rolling-origin analysis and leakage-free experimental design emerge. Moreover, the paper determines the main issues associated with the real-time implementation such as the complexity of the computation, scalability, and integration with the energy management system. It also points out the increased significance of uncertainty judgment by means of probabilistic forecasting methodology. Although this has been recently improved, the gaps in research have not been closed yet, such as cross-market generalization, transparency in what is available in the decision time and interest in evaluating operation value besides mainstream error measures. Lastly, the paper gives the future research paths on how to establish strong, interpretable and uncertainty aware hybrid EPF frameworks to increase the practical applicability of such models to the current smart grid setting.
BERT-LGBM Model for Error and Union Attacks Detection in Web Application Imdad Ali Shah; Noor Zaman Jhanjhi
Journal of ICT, Design, Engineering and Technological Science Volume 10, 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-10.1.5

Abstract

The rapid growth of web-based applications has mostly increased the threats of SQL injection (SQLi) attacks, which remain the most critical risks to data security and system integrity. SQLi is one of the severe and persistent threats to data confidentiality. SQLi attacks exploit vulnerabilities in web apps input fields, permitting adversaries to manipulate queries and obtain unauthorized access to sensitive data. In the modern era, SQL injection attack types have increased, with error-based attacks being the most critical security concerns for web app firewalls. Several industries are vulnerable, such as online banking e-commerce, healthcare, financial institutions and government services. With the growing trust in digital infrastructures, attackers use advanced techniques to exploit vulnerabilities in database queries to obtain illegal access to personal information. Traditional detection systems, such as Static, Dynamic, and Manual Analysis, are insufficient for detecting new methods and SQLi attacks due to their static nature and limited adaptability in webapps traffic. The purpose of this article is to build an AI-based model for detecting accurate and robust SQLi (error-based) attacks. This research aims to give intelligent solutions the ability to secure NLP applications against the complicating and changing attack vectors. Our study contributes to advancing web apps security by giving an effective and scalable AI-based solution for SQLi (error-based) attacks detection. Our proposed model has achieved results, accuracy 0.99, precision 0.98, recall 0.97 and F1 0.99. Outperforms existing approaches in SQL injection (error-based) detection, demonstrating superior performance compared to the RF models. While BERT-LSTM achieved slightly lower performance, accuracy: 0.97, precision: 0.963, recall: 0.962, F1-score: 0.958. The RF model matched the proposed model in accuracy 0.99 and F1-score 0.98 while achieving the highest recall 0.997, indicating a strong detection model. These results highlight the robustness and reliability of the proposed model in balancing precision and recall, making it more effective for real-world SQL injection (error-based) detection tasks.

Page 9 of 9 | Total Record : 87

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