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ITEj (Information Technology Engineering Journals)
Published by Universitas Islam Negeri Siber Syekh Nurjati Cirebon
ISSN : 25482130     EISSN : 25482157     DOI : https://doi.org/10.24235/itej.v5i2
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics
ITEj (Information Technology Engineering Journals) is an international standard, open access, and peer-reviewed journal to discuss new findings in software engineering and information technology. The journal publishes original research articles and case studies focused on e-learning and information technology. All papers are peer-reviewed by reviewers. The scope of the system discussed is attached but not limited; Systems and software engineering Artificial Intelligence Technology (AI) and Machine Learning Internet of Thing and Big Data Smart Education systems and components Computer Vision Information Technology etc
Arjuna Subject : -
Articles 119 Documents
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AI-Driven Urban Planning: Enhancing Efficiency and Sustainability in Smart Cities Hadiyana, Thomas; Ji-hoon, Seo
ITEJ (Information Technology Engineering Journals) Vol 9 No 1 (2024): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.124

Abstract

Urban planning in smart cities is increasingly leveraging artificial intelligence (AI) to enhance efficiency and sustainability. This article explores the integration of AI-driven technologies to optimize various aspects of urban development and management. Smart cities are characterized by their use of advanced technologies to improve quality of life, resource management, and infrastructure efficiency. Traditional urban planning methods often face challenges in adapting to rapid urbanization and dynamic environmental changes. AI presents opportunities to address these challenges by providing data-driven insights and predictive capabilities. This research employs a case study approach, analyzing the implementation of AI in urban planning processes across several smart cities globally. Key methodologies include data analytics, machine learning algorithms, and predictive modeling techniques applied to diverse urban datasets. The study evaluates how AI-driven decision support systems aid in infrastructure planning, traffic management, energy consumption optimization, and environmental sustainability. The findings demonstrate that AI-enabled urban planning significantly enhances efficiency and sustainability in smart cities. AI algorithms optimize traffic flow, reduce energy consumption through predictive maintenance of infrastructure, and facilitate adaptive urban design based on real-time data analytics. Moreover, AI-driven approaches improve decision-making processes by providing stakeholders with actionable insights for informed policy formulation and resource allocation. This article contributes to the evolving field of smart city technologies by showcasing the transformative potential of AI in urban planning. By harnessing AI capabilities, cities can effectively address complex urban challenges and pave the way for more resilient and sustainable urban environments.
Machine Learning for Predictive Maintenance to Enhance Energy Efficiency in Industrial Operations Cruz, Juan Carlos; Garcia, Antonio Miguel
ITEJ (Information Technology Engineering Journals) Vol 9 No 1 (2024): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.125

Abstract

In the realm of industrial operations, optimizing energy usage is critical for both economic and environmental sustainability. Traditional approaches to maintenance often rely on fixed schedules or reactive responses to equipment failures, leading to inefficiencies and higher energy consumption. Predictive maintenance (PdM) offers a proactive solution by leveraging machine learning algorithms to predict equipment failures before they occur. This approach not only reduces downtime but also facilitates energy-efficient practices by enabling timely interventions and optimized operational strategies. This study explores the application of machine learning techniques for predictive maintenance in a manufacturing setting. Historical operational data, including equipment performance metrics and environmental conditions, are collected and preprocessed. Various machine learning models, such as support vector machines (SVM), random forests, and neural networks, are trained on this dataset to predict equipment failures and maintenance needs. Feature engineering and model selection processes are detailed to highlight the steps taken to enhance prediction accuracy and reliability. Through rigorous experimentation and validation, our approach demonstrates significant improvements in energy efficiency within industrial operations. By predicting maintenance needs in advance, downtime is minimized, and energy-intensive emergency repairs are avoided. Furthermore, the implementation of optimized maintenance schedules and operational strategies based on machine learning predictions leads to substantial reductions in overall energy consumption. Case studies and quantitative analyses underscore the efficacy of our methodology in enhancing both operational efficiency and energy sustainability in industrial settings.
From Battlefield to Border: The Evolving Use of Drones in Surveillance Operations Hua, Zhang
ITEJ (Information Technology Engineering Journals) Vol 9 No 1 (2024): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.126

Abstract

In recent decades, drone technology has undergone rapid advancements, making it a vital tool in various surveillance operations. Initially limited to the battlefield, the use of drones has now expanded to various civilian applications, including border monitoring, law enforcement, and environmental surveillance. This shift is driven by enhancements in drone capabilities, including extended range, endurance, and sensor technology. This study employs a qualitative approach using case studies to analyze the evolution of drone usage in surveillance operations. Data were collected through literature reviews, interviews with industry experts, and analysis of reports from security and defense agencies. The study also compares the effectiveness of drone-based surveillance operations with traditional methods through statistical analysis and field operational evaluations. The findings indicate that drone usage significantly enhances the efficiency and effectiveness of surveillance operations. Drones enable wider area coverage at lower costs and reduced risks compared to conventional methods. Additionally, drones equipped with advanced sensors facilitate more accurate and real-time data collection, which is crucial in critical security situations. The study also identifies major challenges in drone usage, including regulatory issues, privacy concerns, and the integration of technology with existing systems. The evolving use of drones in surveillance operations shows great potential in enhancing security and efficiency across various sectors. However, a balanced approach between technological innovation and regulatory frameworks is necessary to address existing challenges and maximize the benefits of this technology.
Development of an Operating System Supporting Intelligent Predictions and Recommendations Rahman, Rakhmadi; Apriliyani, Alya Wulan; Ibrahim, Siti Nur Azizah
ITEJ (Information Technology Engineering Journals) Vol 9 No 1 (2024): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.127

Abstract

This Study discusses the development of an intelligent operating system feature that supports smart prediction and recommendations using artificial intelligence (AI) capabilities within the Linux operating system. The study aims to integrate AI-driven features into Linux to enhance user productivity and efficiency by providing relevant application recommendations based on user behavior patterns. The implementation involves data collection of application usage, training machine learning models for application recommendations, and integrating these features into the Linux environment. The project utilizes Python for scripting, employing libraries such as psutil, pandas, scikit-learn, and joblib for data handling and machine learning tasks. The results demonstrate successful implementation of the AI-driven recommendation system, enhancing user interaction and productivity within the Linux operating system
Integration of Information Technology and Machine Learning to Improve the Efficiency of IoT-Based Logistics Systems Amelia, Maya; Hudaya, Agus
ITEJ (Information Technology Engineering Journals) Vol 9 No 1 (2024): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.132

Abstract

In today's digital era, efficiency in supply chain management and logistics is the main key to maintaining business competitiveness. This article discusses the integration of Information Technology (IT) and Machine Learning (ML) in Internet of Things (IoT)-based logistics systems to improve operational efficiency. By leveraging IoT sensors for real-time data collection and ML algorithms for predictive analysis, the system is able to optimize inventory management, route planning, and preventive maintenance. The case studies discussed in this article show that the use of ML in IoT-based logistics systems can reduce delivery times, lower operational costs, and increase responsiveness to changes in market demand. The results of this study are expected to provide insight for system developers and logistics managers in implementing advanced technologies to address challenges in the modern logistics industry.
Design and Implementation of Network Security Systems on Virtualized Networks Syam, Akmal Baharuddin; Rahman, Rakhmadi
ITEJ (Information Technology Engineering Journals) Vol 9 No 2 (2024): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.128

Abstract

This report, entitled "Design and Implementation of Network Security Systems on Virtualized Networks," was prepared to fulfill the final assignment of the Network Security course at the Bacharuddin Jusuf Habibie Institute of Technology (ITH). This research aims to design, implement and identify network security vulnerabilities in a virtualization environment using Proxmox Virtual Environment (Proxmox VE) in VirtualBox. The research results show that Proxmox VE in VirtualBox is less successful in optimizing software-hardware resources by implementing security mechanisms such as firewalls, encryption, IDS/IPS, VPN, and IAM. Even though it has several shortcomings, Proxmox VE has proven to be effective in managing virtual networks safely and efficiently when carried out outside of VirtualBox. This research also provides practical experience for students in implementing and identifying network security vulnerabilities, preparing them for real-world challenges.
Cross-Domain Transfer Learning: Enhancing Deep Neural Networks for Low-Resource Environments Cruz, Maria Elena; Miguel, David
ITEJ (Information Technology Engineering Journals) Vol 9 No 2 (2024): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.136

Abstract

Deep neural networks (DNNs) have achieved remarkable success in various domains; however, their performance often relies heavily on large-scale, high-quality labeled datasets, which are scarce in low-resource environments. Cross-domain transfer learning has emerged as a promising technique for adapting pre-trained models from data-rich source domains to low-resource target domains to address this limitation. This study explores innovative strategies to enhance the performance and applicability of DNNs through cross-domain transfer learning, focusing on challenges such as domain disparity, data scarcity, and computational constraints. We evaluate several transfer learning approaches, including feature-based transfer, parameter fine-tuning, and adversarial domain adaptation, across diverse healthcare, agriculture, and natural language processing applications. Experimental results demonstrate significant improvements in model accuracy and generalization in low-resource environments, with accuracy gains of up to 20% compared to models trained from scratch. Additionally, we analyze the impact of transfer learning on reducing training time and computational requirements, making it a viable solution for resource-constrained settings. Despite its potential, the study highlights critical challenges, including negative transfer, model interpretability, and ethical considerations in domain transfer. Addressing these issues, we propose a framework for selecting optimal source domains and enhancing model robustness through hybrid techniques and unsupervised learning. This research emphasizes the transformative potential of cross-domain transfer learning in bridging the gap between data-rich and low-resource environments, paving the way for more equitable and efficient applications of deep learning technologies worldwide.
Integrating IoT and Artificial Intelligence for Sustainable Smart City Development: A Case Study Approach Alwar, Benedicto; Edgardo, Hector; Faustino, Edwardo
ITEJ (Information Technology Engineering Journals) Vol 9 No 2 (2024): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.137

Abstract

Rapid urbanization growth has intensified the demand for sustainable smart city solutions that optimize resource management, improve citizens’ quality of life, and reduce environmental impact. Integrating Internet of Things (IoT) technology with artificial intelligence (AI) offers transformative opportunities to address these challenges by enabling real-time data collection, analysis, and decision-making. This study explores the potential of integrating IoT with AI for sustainable smart city development, using a case study approach to examine its application in diverse urban domains, including energy management, transportation, waste management, and public safety. The research highlights innovative IoT-enabled systems such as smart grids, intelligent traffic control, predictive waste collection, and AI-driven surveillance, demonstrating their ability to improve efficiency and sustainability. Case studies from globally recognized smart cities such as Singapore, Barcelona, ​​and Copenhagen illustrate the benefits and challenges of adopting these technologies. Key findings reveal significant improvements in energy efficiency (up to 25%), reduced traffic congestion (up to 30%) and optimised waste management (up to 40%). However, challenges such as data privacy, interoperability and high implementation costs remain barriers to large-scale deployment. This study proposes a framework to address these issues, emphasizing collaborative governance, robust cybersecurity measures and scalable infrastructure design. The findings underline the transformative potential of integrating IoT and AI to achieve sustainable urban development, offering practical insights for policy makers, urban planners and technology developers. This research contributes to driving smart city initiatives by bridging technological innovation with sustainability goals, paving the way for more resilient and liveable urban environments.
Optimizing Loan Approval Processes with Support Vector Machines (SVM) Angraini, Novita; Rosalina, Kelly; Kosasih, Andini
ITEJ (Information Technology Engineering Journals) Vol 9 No 2 (2024): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.138

Abstract

Loan approval is a critical process in banking, requiring accurate assessment of borrower risk to minimize defaults while maintaining customer satisfaction. This study explores the optimization of loan approval processes using Support Vector Machines (SVM), a robust machine learning method known for its effectiveness in classification tasks. We utilized a dataset comprising historical loan applications, incorporating features such as credit score, income level, debt-to-income ratio, and employment history. The SVM model was trained and evaluated using cross-validation techniques to ensure generalizability. Our results demonstrate that SVM outperforms traditional statistical methods in predicting loan approval decisions, achieving higher accuracy and a significant reduction in false positives. Furthermore, feature importance analysis revealed that credit score and debt-to-income ratio are the most influential factors in the model's decision-making process. By integrating the optimized SVM model into existing banking workflows, institutions can streamline their approval processes, reduce operational costs, and improve customer experience. This study highlights the potential of SVM in modernizing decision-making frameworks in the banking sector, paving the way for further adoption of advanced machine learning techniques in financial services.
A Review: Development of an IoT-Based Smart Home Monitoring System for the Comfort of People with Disabilities Anjela, Rara; Pertiwi, Kisna; Sabar, Sabar
ITEJ (Information Technology Engineering Journals) Vol 9 No 2 (2024): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.190

Abstract

The advancement of the Internet of Things (IoT) has revolutionized smart home technology, providing enhanced comfort, security, and accessibility, particularly for people with disabilities. This review explores the development of IoT-based smart home monitoring systems designed to improve the quality of life for individuals with mobility, sensory, or cognitive impairments. By integrating IoT sensors, automation, and artificial intelligence (AI), smart home systems can provide real-time monitoring, adaptive control of household appliances, and emergency response mechanisms. The study highlights key technologies such as voice-controlled assistants, smart sensors, and remote accessibility features, which enable seamless interaction with home environments. Additionally, challenges related to data privacy, security risks, and affordability are discussed, along with potential solutions. The findings suggest that IoT-enabled smart home systems significantly enhance the independence and well-being of individuals with disabilities, emphasizing the need for continued innovation and policy support in this field. Keywords— Disability, Smart Home, Google Assistant, IoT.

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