cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Juhriyansyah Dalle
Contact Email
jitdets@gmail.com
Phone
-
Journal Mail Official
jitdets@gmail.com
Editorial Address
-
Location
Kab. banyumas,
Jawa tengah
INDONESIA
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
 5   6   7   8   9 
Smart Gas Leak Detection And Emergency Response System Using Iot For Homes Abdul Salam Shah; Amar Dinesh; Asadullah Shah; Mirza Farooq; Adil Maqsood; Muhammad Adnan Kaim Khani
Journal of ICT, Design, Engineering and Technological Science Volume 8, 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-8.1.3

Abstract

As we know, safety is a massive problem in this world today. We can use technology to combat the issue of safety. One of the safety issues is gas leakage, which caused the accident. In this project, we design and develop a system that is based on IoT and detects and monitors gas leakage in real time in homes and small businesses. The project uses NodeMCU as a microcontroller, gas sensors, and other devices like the Wi-Fi module, servo motor, and exhaust fans. This project shows how to integrate different hardware components and hardware with software. The traditional gas detectors found in the market can only alert the user through audio and visual alerts that are only viable if a person is present to combat the issue; what this project does is not only alerts using audio and video, it also alerts the user and the emergency department using a notification sent to an application in the mobile phone. The integration of the app not only increases user interface experience and responsive time but also allows the user to adjust the system's parameters through the app and gives real-time status to prevent accidents; the project also deploys prevention measures such as opening the window, turning on the exhaust, and shutting off the main gas valve to avoid chances of fireand damage.
Evaluating Machine Learning Models for Real-Time IoT Intrusion Detection: A Comparative Study with RTSS Analysis Ahmed Alwan; Asadullah Shah; Alwan Abdullah Abdul Rahman Alwan; Shams Ul Arfeen Laghari
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-8.2.1

Abstract

With the ever-increasing sophistication and volume of cyber-attacks, there is a critical need for effective intrusion Detection Systems (IDS) to protect computer networks. Machine Learning (ML) offers powerful tools for IDS by automatically identifying patterns of malicious behavior. This research proposal aims to evaluate and compare the performance of several supervised ML algorithms for network threat detection using the CICIDS 2023 dataset. This paper focuses on widely-used classifiers—logistic regression, Support Vector Machine (SVM), Random Forest, eXtreme Gradient Boosting (XGBoost), and k-Nearest Neighbors (KNN) – applied to both binary (benign vs. attack) and multi-class (multiple attack types) classification tasks. This paper outlines a methodology for data preprocessing, model training, and performance evaluation using metrics like accuracy, precision, recall, and F1-score. By leveraging the comprehensive CICIDS 2023 intrusion dataset, which includes 33 modern attack scenarios across seven categories, this paper expects to gain insights into the relative strengths of each ML approach in detecting diverse cyber threats. The anticipated outcome is an identification of which algorithms (or combination thereof) are most promising for intrusion detection in contemporary network environments, guiding future developments of intelligent IDS. This proposal details the problem motivation, related work, planned methodology, and expected results, establishing a foundation for a thorough experimental study.
Exploring the AI-powered Adoption in Higher Education: A Comprehensive Study Using UTAUT4 Model to Understand User Acceptance and Usage Sajeela Ashfaque Tago; Ayaz Keerio; Shahmurad Chandio; Altaf Hussain Abro
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-8.2.2

Abstract

AI-powered learning is an innovative, student-centered educational paradigm integrating formal, informal, and social learning modalities. This study examines the acceptance of AI-powered learning in higher education institutions in Pakistan, concentrating on student acceptance and usage. Contextual awareness, self-directed learning, Personal innovativeness, and performance expectancy Factors were examined using the Smart-PLS approach to evaluate structural relationships and test hypotheses based on the expanded Unified Theory of Acceptance and Use of Technology (UTAUT4). The Results indicate substantial positive correlations between the proposed variables and students' acceptance of AI-powered learning methods. The findings offer significant insights into the structures that may influence the utilization and subsequent outcomes of AI-powered learning acceptance & usage in HEI, including Pakistan, and the UTAUT4 model offers a useful guide for decision-makers and educational institutions working on m-learning adoption at universities.
Intelligent Vehicle Number Plate Recognition System Using Yolo For Enhanced Security In Smart Buildings Muhammad Adnan Kaim Khani; Muhammad Usama; Abdul Salam Shah; Asadullah Shah; Syed Hyder Abbas; Adil Maqsood; Asif Ali Laghari
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-8.2.3

Abstract

The demand for advanced security solutions has increased with the continuous growth of urban infrastructure; hence, automated surveillance systems are vital across universities, hospitals, and commercial spaces. This project proposes an end-to-end Automatic Number Plate Recognition (ANPR) system to identify vehicle license plates by capturing high-speed images under optimal lighting conditions, isolating and analyzing plate characters, and translating the visual data into machine-readable text. By deploying these models on embedded systems, the system uses Convolutional Neural Networks (CNNs) and YOLO (You Only Look Once) for real-time object detection and recognition. The solution leverages the power of edge computing to achieve high performance and low latency for effective vehicle monitoring, data logging, and enhancing overall security infrastructure in buildings.
An Android-Based Information System for Enhancing Rural Transportation Services in Banjarnegara, Indonesia Abdan Zaki Alifian; Ridho Muktiadi; Tito Pinandita; Ermadi Satriya Wijaya
Journal of ICT, Design, Engineering and Technological Science Volume 8, 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-8.1.4

Abstract

Transportation is the movement of people or goods from one place to another using a tool driven by humans or machines. The large number of people in Banjarnegara Regency makes the need for rural transportation in Banjarnegara regency very important, especially for those who do not have private vehicles to travel from one place to another. Mobile technology is developing very fast, both in terms of hardware and software. Mobile-bile technology can now be used in various
The Analysis of the UI/UX of Mobile Devices on the LAZNAS AL IRSYAD Website Using the User-Centered Design Method Muhammad Ikhfil Khusen; Achmad Fauzan; Ridho Muktiadi; Mukhlis PrasetyoAji
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-8.2.4

Abstract

The research aims to analyze the User Interface (UI) and User Experience (UX) aspects of the LAZNAS (National Amil Zakat Institution) AL IRSYAD website, specifically when accessed via mobile devices. Employing a User-Centered Design (UCD) approach, the study places users at the core of the design process to identify their needs and the primary issues they encounter. The evaluation was carried out using the System Usability Scale (SUS) method to measure usability and user satisfaction. The findings revealed several shortcomings in the website's initial design, such as non-functional menu buttons, inconsistent icons, and poorly structured page layouts. The initial SUS score was 73.49, categorized as "good," but interviews revealed that several usability issues remained unresolved. Following the analysis, a redesign was conducted based on the findings, resulting in a new prototype created using Figma with a screen size 360x800. The prototype was re-evaluated, yielding an improved SUS score of 83.62, categorized as "excellent." This study is expected to provide design recommendations that address technical issues and enhance the overall user experience. Furthermore, the findings can serve as a reference for LAZNAS AL IRSYAD to optimize their website services.
Securing Access to Academic Information Systems with WhatsApp-Based OTP: Implementation at Senior High School Nelsen Septa Henidar; Achmad Fauzan; Ridho Muktiadi; Lahan Adi Purwanto Wijaya; Tresna Yudha Prawira
Journal of ICT, Design, Engineering and Technological Science Volume 8, 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-8.1.5

Abstract

Development transformation in the digital age provides some changes, one of which is in security aspects. One of the methods that can enhance security is applying the two-factor authentication (2FA) method through a one-time password (OTP) code as an extra level of credibility. The use of WhatsApp API through a third-party API provider named Fonnte was applied to the college information system of Purwokerto Senior High School. With its integration of Fonnte API, it can deliver OTP codes in real-time within an average time delay of 1.2 seconds, which is sent through WhatsApp, a favorite instant app that is easily accessed through social groups. The implementation in question is made in order to enhance user data security and avoid unauthorized access. The result of the experiment reveals that two-factor authentication use through OTP is successful in sending OTP within a short response time and achieving results through administering a questionnaire for 10 respondents, which acquired 82.6 results in the category of Strongly Agree.
Enhancing Residential Safety and Comfort Through Smart Home Security and Automation Technologies Shahbaz Ali Khan; Shahjahan Samoo; Abdul Salam Shah; Adil Maqsood; Muhammad Adnan Kaim Khani; Asadullah 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-8.2.5

Abstract

In the digital era, technology is changing rapidly, and humans are trying to make lives easier, but it brings a new challenge: security. Computer programs or developed hardware can be compromised if not appropriately designed or because of the simple mistakes of an authorized person. The project aims to secure a home using face recognition to unlock the doors and alarm in an emergency. The home security automation technology uses a wireless network to support the alarm and deactivation requirements. The face detection unit uses an internetconnection via an ESP32 CAM; the primary controlled systems are utilized with Wi-Fi technologies. ESP32 manages home electronic appliances and camera devices, featuring a cost-effective structure, easy-to-use interface, and simple deployment. In this project, the system primarily fulfills home security demands using face-detection gadgets, utilizing a controller with a camera. The device can manage a high-power scoring load using security locks.
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.

Page 7 of 9 | Total Record : 82

 5   6   7   8   9 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Volume 9, Issue 2 Volume 9, Issue 1 Volume 8, Issue 2 Volume 8, Issue 1 Volume 7, Issue 2 Volume 7, Issue 1 Volume 6, Issue 2 Volume 6, Issue 1 Volume 5, Issue 2 Volume 5, Issue 1 Volume 4, Issue 2 Volume 4, Issue 1 Volume 3, Issue 2 Volume 3, Issue 1 Volume 2, Issue 2 Volume 2, Issue 1 Volume 1, Issue 2 Volume 1, Issue 1 More Issue