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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 3 Documents
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Search results for , issue "Volume 9, Issue 1" : 3 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.

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