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International Journal of Cyber and IT Service Management (IJCITSM)
Published by Pandawan Sejahtera Indonesia
ISSN : 27971325     EISSN : 2808554X     DOI : https://doi.org/10.34306/ijcitsm
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
International Journal of Cyber and Service Management (IJCITSM) is an international peer-reviewed journal that publishes high quality and refereed papers which reports original research and innovative applications in all areas of Computer Science, Informatics, Electronics Engineering, Communication Network and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal also seeks clearly written survey and review articles from experts in the field, to promote insightful understanding of the state-of-the-art and technology trends.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Sistem Informasi Menejemen
Articles 10 Documents
 1 
Search results for , issue "Vol. 4 No. 2 (2024): October" : 10 Documents clear
Enhancing Predictive Models in System Development Using Machine Learning Algorithms Muhammad Hatta; Wahyu Nur Wahid; Faisal Yusuf; Farhan Hidayat; Nesti Anggraini Santoso; Qurotul Aini
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.159

Abstract

Predictive models play a crucial role in system development, enabling more informed decision making and improving system efficiency. However, traditional predictive models often struggle with scalability and accuracy in complex environments. This paper explores the use of Machine Learning (ML) algorithms to enhance predictive models, offering more accurate and scalable solutions. By leveraging key ML techniques such as decision trees, regression models, and neural networks, the study demonstrates how these algorithms can improve predictive accuracy and system performance. The methodology involves data collection, model training, and performance evaluation using various metrics to assess the effectiveness of ML enhanced predictive models. The results indicate a significant improvement in model accuracy and scalability, making ML a valuable tool in advancing system development processes. By incorporating ML frameworks specifically tailored to the unique demands of system development, this research offers new methodological adaptations designed to optimize scalability and performance. This study diverges from previous research by implementing and tailoring ML techniques uniquely suited for complex system development environments, enhancing both predictive accuracy and scalability.
Challenges in Securing Data and Networks from Modern Cyber Threats ⁠⁠⁠Irma Shantilawati; Jihan Zanubiya; Fajriannoor Fanani; Henrik Jensen; Shofiyul Millah; Ninda Lutfiani
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.160

Abstract

In the era of digital transformation, organizations face an increasing array of cyber threats that target data and network security. This study focuses on the key challenges encountered in protecting sensitive information and network infrastructures from evolving cyberattacks, such as ransomware, phishing, and advanced persistent threats. The objective is to explore the current landscape of cyber threats and identify the major hurdles in implementing effective security measures. Through a mixed-method approach involving surveys and case studies, the research highlights the skills gap, complexity of network architectures, and the rapid advancement of threats as primary obstacles. The findings underscore the need for advanced security solutions, such as AI-driven threat detection and zero trust models, to enhance organizational resilience. Addressing these challenges is critical to safeguarding data, ensuring business continuity, and maintaining regulatory compliance.
Artificial Intelligence in Autonomous Vehicles: Current Innovations and Future Trends Nuraini Diah Noviati; Fengki Eka Putra; Sadan Sadan; Ridhuan Ahsanitaqwim; Nanda Septiani; Nuke Puji Lestari Santoso
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.161

Abstract

Artificial Intelligence (AI) has become a cornerstone in advancing autonomous vehicles, enabling realtime decision making, object detection, and automation in driving systems. This study aims to explore key AI innovations, including Machine Learning (ML) algorithms, computer vision, and reinforcement learning, that contribute to the development of autonomous vehicles. A qualitative approach} was adopted to analyze both current applications and future innovations of AI in autonomous vehicles. The study highlights various current AI applications in autonomous vehicles, such as automated safety features, advanced navigation systems, and adaptive cruise control. These technologies demonstrate how AI enhances vehicle functionality and improves safety in today driving environment. Looking ahead, AI is expected to enable full autonomy in vehicles, foster integration with smart city infrastructures, and drive innovations in fleet management. These advancements are anticipated to significantly improve vehicle safety, operational efficiency, and the overall user experience, solidifying AI as the fundamental technology for the future of intelligent transportation systems.
Enhancing Market Trend Analysis Through AI Forecasting Models Rosa Lesmana; Indra Wijaya; Efa Ayu Nabila; Harry Agustian; Sipah Audiah; Adam Faturahman
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.162

Abstract

Accurate market trend analysis is crucial for strategic decision making in industries, yet traditional forecasting models often struggle to provide reliable predictions in rapidly changing environments. This study investigates the application of advanced Artificial Intelligence (AI) models Long Short Term Memory (LSTM), Random Forest, Decision Trees, and Support Vector Machines (SVM) to improve the accuracy and robustness of market forecasting. Data was collected from sources like Bloomberg and Yahoo Finance, encompassing stock prices, economic indicators, and sector specific trends over five years. The models were evaluated using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to assess their predictive performance. Results show that AI models, especially LSTM, outperform traditional models like Auto Regressive Integrated Moving Average (ARIMA), offering superior handling of complex temporal dependencies and short term market fluctuations. For instance, LSTM achieved a MAPE of 1.8% and RMSE of 0.045, significantly improving forecast precision over ARIMA. Random Forest and Decision Trees also provided valuable insights into market drivers, adding interpretability to the forecasting process. This research highlights the potential of AI to enhance decision making by offering more accurate, data driven predictions and provides practical guidelines for implementing these models in real world market forecasting. Future research should explore hybrid AI approaches and broader datasets to further enhance forecasting adaptability across diverse market conditions.
Building Efficient IoT Systems with Edge Computing Integration Dini Hidayati; Andriyansah Andriyansah; Galih Putra Cesna; Ahmad Yadi Fauzi; Dwi Apriliasari; Untung Rahardja
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.163

Abstract

The exponential growth of the Web of Things (IoT) is transforming businesses, connecting billions of devices that generate massive amounts of data. However, preparing this data at scale in real time poses significant challenges, including inactivity, transmission capacity constraints, and data blocking in centralized cloud systems. Edge computing has become an urgent solution. It allows data preparation to occur closer to the source, thereby improving operational productivity, reducing idle time, and optimizing transmission capacity. This shift toward local availability reduces the burden on centralized cloud systems, making IoT systems more responsive and robust. This article examines the integration of edge computing with IoT. It highlights the fundamental advances that have made this connection possible. Key applications, such as real-time analytics, vision support, and edge AI, describe how edge computing improves data processing and enhances independent decision-making at the device level. Additionally, we discuss how advances in hardware, orchestration techniques, and machine learning drive the development of edge-enabled IoT environments. By analyzing these current uses, we identify emerging trends that will shape future IoT systems, making them more adaptive, efficient, and resilient to changing data demands. This survey highlights the potential of edge computing to power next-generation IoT systems, providing important insights for businesses looking to support complete control of the devices involved.
The Impact of Information Technology Support on the Use of E-Learning Systems at University Aan Kanivia; Hilda Hilda; Alfri Adiwijaya; Muhammad Faizal Fazri; Sabda Maulana; Marviola Hardini
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.166

Abstract

E-learning technologies at educational institutions like University of Raharja are essential in enabling flexible and accessible education in this period of significant digital revolution. This study examines how Information Technology Support (ITS) affects user satisfaction, perceived usefulness, perceived ease of use, and intention to reuse concerning e-learning systems efficacy. Unlike prior Technology Acceptance Model (TAM) based studies, this research emphasizes the specific role of ITS dimensions, such as infrastructure and technical training, in non Western educational contexts. This provides unique insights for improving e-learning systems in higher education. This study assesses the reactions of 500 users of University of Raharja e-learning system using Structural Equation Modeling (SEM) with the PLS technique. Results indicate that perceived ease of use and perceived usefulness are greatly increased by strong ITS, which in turn has a beneficial effect on User Satisfaction and Intention to Reuse. This study aligns with the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education) by enhancing the accessibility and quality of education through e-learning systems, SDG 9 (Industry, Innovation, and Infrastructure) by emphasizing the importance of robust IT infrastructure and technical training, and SDG 10 (Reduced Inequalities) by addressing the digital divide in non Western educational settings. Additionally, ITSs SDG 17 (Partnerships for the Goals) by encouraging collaboration between educational institutions, technology providers, and policymakers to optimize e-learning outcomes. According to the study findings, ITS must be strengthened to maximize e-learning system performance, raise user happiness, and promote continuous usage.
The Role Information Technology in Increasing the Effectiveness Accounting Information Systems and Employee Performance Aoliyah Firasati; Fadhila Azzahra; Sausan Raihana Putri Junaedi; Amelia Evans; Muchlisina Madani; Fitra Putri Oganda
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.167

Abstract

This study explores the mportant role of Information Technology (IT) in enhancing the effectiveness of Accounting Information Systems (AIS) and improving employee performance, using a moderator analysis approach. The context of the study is the increasing dependence of modern organizations on AIS for accurate financial data management and decision-making. However, the effectiveness of these systems can be influenced by various factors, including technological advancement and employee skills. The research methodology includes quantitative analysis, using survey data from various organizations in different sectors. Structural equation modeling (SEM) was applied to examine the moderating effect of IT on the relationship between AIS effectiveness and employee performance. The results of the study show a significant positive impact of IT on AIS effectiveness, leading to increased employee performance. These findings contribute to the literature by highlighting the importance of IT interventions in optimizing AIS functionality, thereby improving organizational efficiency and productivity. In conclusion, this study highlights the essential role of IT as a supporting tool to achieve higher levels of efficiency in accounting information systems and employee performance and highlights the need for continued technological advancement and employee training in the modern business environment.
The Importance Increasing Attendance Efficiency Accuracy with Presence System in Era Industrial Revolution 4.0 Tri Hartono; Bintang Nandana Henry; Sirje Nurm; Lukita Pasha; Dwi Julianingsih
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.168

Abstract

Employee attendance management systems have become a major focus of the Industrial Revolution 4.0 era due to their significant role in increasing organizational productivity and performance. This study demonstrates the importance of the SmartPLS methodology in analyzing the impact of IoT based attendance technology and big data analytics on the efficiency and accuracy of employee attendance. Both the assessments reviewed show that the use of IoT based attendance technology and the implementation of big data analytics systems have a significant positive impact on the efficiency and accuracy of employee attendance. IoT based attendance technology enables real-time attendance data collection with high accuracy, while big data analytics enables organizations to derive valuable insights from the large volume of collected attendance data. These findings provide a better understanding of the contribution of technology in increasing organizational productivity and performance in today digital age. This study provides valuable insights for business professionals and academics to develop adaptive and effective attendance management strategies. Using IoT based attendance technology and big data analytics, organizations can improve operational efficiency, increase payroll accuracy, and optimize overall human resource utilization. Furthermore, the study also highlights the importance of adapting and innovating in the face of technological developments. By incorporating knowledge of the latest technology and industry trends, organizations can continuously enhance their attendance management strategies to remain relevant and competitive in the ever changing business environment.
The Impact of War on The Cryptocurrency Economy from a Management Perspective Suryari Purnama; Bayu Laksma Pradana; Gautam Khanna; Suhandi Suhandi; Agung Rizky; Ihsan Nuril Hikam; Muhammad Farhan Kamil
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.169

Abstract

Armed conflicts and wars are increasingly shaping the global economic landscape, impacting both traditional markets and the burgeoning cryptocurrency economy. Cryptocurrencies, underpinned by blockchain technology, hold revolutionary potential for transactions, investments, and trading. However, their decentralized and global nature leaves them vulnerable to external shifts, particularly geopolitical events like war. This research explores the influence of war on cryptocurrency from a management perspective, analyzing how conflict impacts regulation, investment patterns, and technology adoption within the cryptocurrency ecosystem. By employing a literature based approach, this study aims to elucidate how global political and security shifts affect the cryptocurrency market. The findings indicate high reliability in the observed variables Investor, Crypto Market, and War with Cronbach alpha values ranging from 0.832 to 0.878, and rhoA values between 0.860 and 0.881. Additionally, composite reliability scores are robust, ranging from 0.860 to 0.882, demonstrating strong measurement reliability. The Average Variance Extracted (AVE) values, between 0.603 and 0.673, confirm that these measurement variables significantly explain the variance of the latent constructs. These results underscore the efficacy of the developed model in analyzing the interplay between war and cryptocurrency markets, contributing valuable insights into the sector resilience and adaptability amid geopolitical conflicts.
Long Term Aging Effects on Polymer Materials Photovoltaic Modules Durability and Safety Eli Ratih Rahayu; Raihan Raihan; Zinhle Ndlovu; Sondang Visiana Sihotang; Najalia Malika; Anandha Fitriani
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.170

Abstract

This study investigates the long term aging effects on polymer materials used in Photovoltaic (PV) modules, with a focus on evaluating their durability, reliability, and safety over extended operational periods. Polymers in PV modules play a critical role in encapsulating and protecting sensitive components from environmental exposure, but they are also subject to degradation due to prolonged exposure to UV radiation, temperature fluctuations, and moisture. This research assesses the aging mechanisms affecting polymer performance, utilizing accelerated aging tests that simulate various environmental conditions to predict material lifespan under real world conditions. Through a combination of thermal, mechanical, and chemical analyses, this study identifies key degradation factors and evaluates their impact on the structural integrity and functionality of PV modules. Findings reveal significant correlations between specific aging stressors and the degradation of polymer materials, which may contribute to efficiency loss and safety risks over time. This study distinguishes itself from existing Technology Acceptance Model (TAM) based e-learning studies by focusing on a comprehensive analysis of polymer degradation mechanisms specific to photovoltaic modules. Unlike prior studies, it evaluates real world conditions through a blend of mechanical, thermal, and chemical analyses, offering unique insights into improving PV system reliability. This research provides insights that can guide manufacturers and engineers in optimizing polymer materials for sustainable and safer PV module applications, particularly in climates with harsh environmental conditions.

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