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p-Index From 2021 - 2026
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All Journal Journal of Computer Science and Technology Application
Ramzi Zainum Ikhsan
University of Raharja
Computer Science & IT Other

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Cybersecurity in the Age of IoT and Developing Frameworks for Securing Smart Devices and Networks Eli Ratih Rahayu; Ariesya Aprillia; Ramzi Zainum Ikhsan; Alfri Adiwijaya; Aryan Kumara
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.64

Abstract

The rapid proliferation of the Internet of Things (IoT) has significantly transformed various industries, enhancing automation and efficiency. However, it has also brought forth substantial cybersecurity challenges that threaten data integrity, user privacy, and system reliability. This study proposes a multi-layered cybersecurity framework to address these vulnerabilities by integrating robust security measures such as device authentication, data encryption, continuous network monitoring, and enhanced privacy protection. Employing a mixed methods research approach, the framework was rigorously validated through real world implementation in smart home environments, demonstrating tangible improvements in security resilience. Notably, the findings indicate a 40% reduction in threat response time, a 96% intrusion detection rate, and the complete elimination of data breaches post-implementation, emphasizing the framework’s effectiveness in mitigating cyber risks. Proactively addressing security concerns, this study provides valuable insights for key stakeholders, including device manufacturers, network operators, and policymakers, guiding them toward implementing stringent cybersecurity protocols to enhance trust and compliance across IoT ecosystems. Furthermore, the results highlight the necessity for continuous adaptation and innovation in cybersecurity strategies, ensuring that IoT deployments remain resilient against evolving cyber threats. As IoT adoption continues to accelerate across sectors such as healthcare, smart cities, and industrial automation, this research underscores the critical importance of a proactive, comprehensive security approach to safeguard connected infrastructures. Ultimately, the proposed framework serves as a blueprint for strengthening IoT security governance and fostering a safer digital ecosystem, reinforcing the importance of collaborative efforts in securing the future of interconnected technologies.
Revolutionizing Renewable Energy Systems throughAdvanced Machine Learning Integration Approaches Sri Rahayu; Nanda Septiani; Ramzi Zainum Ikhsan; Yasir Mustafa Kareem; Untung Rahardja
CORISINTA Vol 2 No 2 (2025): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i2.115

Abstract

The increasing global emphasis on sustainability has accelerated investments in renewable energy technologies, positioning sources like solar, wind, and hydroelectric power as vital alternatives to fossil fuels. Despite significant progress, integrating renewable energy into existing grids remains challenging due to variability in energy output, grid instability, and inefficiencies in energy storage systems. This study investigates the potential of machine learning (ML) to revolutionize the renewable energy sector by enhancing energy forecasting, grid management, and energy storage optimization. Using a combination of supervised learning, deep learning, and reinforcement learning techniques, we developed predictive and optimization models based on historical and real-time datasets. Additionally, structural equation modeling (SEM) with SmartPLS was employed to analyze the relationships between key variables, such as machine learning algorithms, renewable energy sources, sustainability performance, and operational efficiency. The results indicate that machine learning significantly improves energy forecasting accuracy, grid reliability, and storage efficiency, with R-squared values of 0.685 for operational efficiency and 0.588 for sustainability performance. These findings highlight the transformative role of ML in optimizing renewable energy systems and achieving sustainable energy goals. While ML offers promising solutions for renewable energy challenges, further research is needed to address real-time data integration, model scalability, and economic feasibility. This study provides a foundation for future innovations, emphasizing the importance of intelligent, data-driven strategies in advancing global energy sustainability.
Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques Eryc; Nasib; Muh. Fahrurrozi; Ramzi Zainum Ikhsan; Jonathan Parker
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v3i1.148

Abstract

This study, titled Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques, explores how artificial intelligence (AI) particularly machine learning (ML) can enhance the accuracy and strategic impact of business forecasting in dynamic markets. Traditional statistical forecasting methods often fail to accommodate complex, nonlinear, and high-dimensional data. To address this gap, the research develops and validates a machine learning–based forecasting model designed to integrate predictive analytics into strategic decision-making. The study adopts a quantitative approach and employs Structural Equation Modeling (SEM) using SmartPLS 3 to examine the interrelationships among four latent variables: Market Trends (MT), Forecasting Accuracy (FA), Strategic Planning Efficiency (SPE), and Business Performance (BP). Each construct is measured using three indicators, forming a structural model that tests six hypothesized relationships. The results indicate that understanding market trends significantly improves forecasting accuracy and strategic planning efficiency, which in turn positively influences business performance. Furthermore, forecasting accuracy directly enhances both planning efficiency and overall performance, emphasizing the strategic value of data-driven insights. The findings validate the reliability and predictive power of the proposed model, offering a robust framework for organizations aiming to leverage machine learning in strategic forecasting. By bridging the gap between algorithmic prediction and managerial application, this study contributes to the growing field of AI-driven business analytics and supports the development of more agile, informed, and resilient business strategies in a data-centric economy.
Co-Authors Alfri Adiwijaya Ariesya Aprillia Aryan Kumara Eli Ratih Rahayu Eryc Jonathan Parker Muh. Fahrurrozi Nanda Septiani Nasib Sri Rahayu Untung Rahardja Yasir Mustafa Kareem