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All Journal Aptisi Transactions on Management Journal of Computer Science and Technology Application
Ahsanitaqwim, Ridhuan
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Computer Science & IT Decision Sciences, Operations Research & Management Other

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Navigating the Challenges of Digital Transformation in Traditional Organization Maratis, Jerry; Ramadan, Ahmad; Rahmania Az Zahra, Achani; Ahsanitaqwim, Ridhuan; Bennet, Daniel
APTISI Transactions on Management (ATM) Vol 8 No 3 (2024): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i3.2349

Abstract

Digital transformation has become a critical strategy for traditional organiza- tions to maintain competitiveness in an increasingly technology-driven market. Technologies such as fintech, blockchain, artificial intelligence (AI), and cloud computing have significantly reshaped operational efficiency and customer en- gagement within these organizations. However, traditional organizations, characterized by their legacy systems and hierarchical structures, encounter various challenges in adopting these technologies. This study primarily aims to explore the key barriers that hinder digital transformation in traditional organizations and to propose effective strategies for overcoming these challenges. Utilizing a comprehensive literature review from 2018 to 2023, this research examines key studies on digital transformation in traditional business contexts. The find- ings reveal major challenges, including organizational inertia, skills gaps, de- pendency on outdated systems, and leadership deficiencies. To address these barriers, the study proposes strategies such as leadership development, work- force retraining, and investment in modern digital infrastructure. The results suggest that successful digital transformation requires a multifaceted approach, aligning technological adoption with organizational culture and sustainability goals. This research provides valuable insights for traditional organizations nav- igating the complexities of digital transformation.
The Role of Automation and IoT in Enhancing Operational Efficiency: Evidence from PLS-SEM Analysis Suhandi; Purnama, Suryari; Nurm, Sirje; Ahsanitaqwim, Ridhuan
APTISI Transactions on Management (ATM) Vol 9 No 1 (2025): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v9i1.2418

Abstract

In today's competitive business environment, operational efficiency is crucial for organizations to maintain a competitive edge. The integration of automation and the Internet of Things (IoT) has emerged as a transformative approach to streamline processes and enhance productivity. However, the synergistic impact of these technologies on operational efficiency remains underexplored. This study aims to evaluate the individual and combined effects of automation and IoT on operational efficiency. It seeks to provide empirical evidence on how these technologies contribute to optimizing workflows and decision-making processes. Methodology Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from organizations across multiple industries. Constructs were measured through validated survey instruments, and hypotheses were tested for direct and synergistic effects. The findings indicate that automation significantly enhances operational efficiency by reducing errors and improving process consistency. IoT adoption complements this by enabling real-time insights and improved decision-making. The combined implementation of these technologies demonstrates a moderate synergistic effect, amplifying operational gains. This study underscores the transformative potential of integrating automation and IoT. By leveraging their complementary strengths, organizations can achieve higher levels of efficiency, providing valuable guidance for digital transformation strategies. In today's competitive business environment, operational efficiency is crucial for organizations to maintain a competitive edge. The integration of automation and the Internet of Things (IoT) has emerged as a transformative approach to streamline processes and enhance productivity. However, the synergistic impact of these technologies on operational efficiency remains underexplored. This study aims to evaluate the individual and combined effects of automation and IoT on operational efficiency. It seeks to provide empirical evidence on how these technologies contribute to optimizing workflows and decision-making processes. Methodology Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from organizations across multiple industries. Constructs were measured through validated survey instruments, and hypotheses were tested for direct and synergistic effects. The findings indicate that automation significantly enhances operational efficiency by reducing errors and improving process consistency. IoT adoption complements this by enabling real-time insights and improved decision-making. The combined implementation of these technologies demonstrates a moderate synergistic effect, amplifying operational gains. This study underscores the transformative potential of integrating automation and IoT. By leveraging their complementary strengths, organizations can achieve higher levels of efficiency, providing valuable guidance for digital transformation strategies.
Advancements and Challenges in the Implementation of 5G Networks: A Comprehensive Analysis Mahyuni; Bimantara, Ade Arya; Nurfaizi, Rifky; Ahsanitaqwim, Ridhuan; Victorianda
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.32

Abstract

The evolution of cellular networks from 1G to 5G has introduced significant advancements in speed, capacity, and reliability. Now, 5G is set to transform communication technology further with higher speeds, increased capacity, reduced latency, and massive IoT connectivity. This research aims to identify the opportunities and challenges in the implementation of 5G networks, focusing on improvements in network speed and capacity, IoT development, industrial applications, user experience, and infrastructure, security, privacy, regulatory, and spectrum challenges. A mixed-methods approach was used, combining qualitative and quantitative analyses. Data were collected from primary sources (expert interviews, surveys) and secondary sources (academic literature, industry reports). Thematic analysis and descriptive and inferential statistics were applied. 5G significantly enhances network speed and capacity, enabling faster, more reliable communication and greater device connectivity. It supports industrial automation, operational efficiency, and innovation in sectors like healthcare, automotive, and manufacturing. Despite its potential, 5G faces challenges such as high infrastructure costs, coverage issues, and security risks. Effective collaboration between government and industry, prioritizing advanced technologies, and developing a comprehensive 5G ecosystem are essential for successful implementation.
Artificial Intelligence in Predictive Cybersecurity: Developing Adaptive Algorithms to Combat Emerging Threats Sudaryono, Sudaryono; Pratomo, Rusdi; Ramadan, Ahmad; Ahsanitaqwim, Ridhuan; Fletcher, Eamon
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.v2i1.55

Abstract

The exponential growth of digital systems has introduced significant cybersecurity challenges, exposing vulnerabilities to increasingly sophisticated threats. Traditional security measures, which rely on static and signature-based methods, often fail to adapt to the dynamic nature of cyberattacks, highlighting the need for innovative solutions. This study aims to develop and evaluate adaptive algorithms in predictive cybersecurity, leveraging Artificial Intelligence (AI) to combat emerging threats such as zero-day exploits and advanced persistent threats (APTs). A simulation-based research design was employed, integrating reinforcement learning frameworks like Deep Q-Learning and utilizing datasets such as CICIDS2017 and synthetic data for zero-day threat simulations. The results show that adaptive algorithms achieved 94.8% detection accuracy, reduced false positives by 54.5%, and improved response times by 53.1%, significantly outper forming static models. Additionally, the adaptive systems demonstrated superiorcapacity to identify novel threats in simulated attack scenarios. These findings underscore the potential of adaptive AI algorithms to revolutionize predictive cybersecurity by offering dynamic, real-time responses to evolving threats. Despite their computational demands posing challenges for smaller organizations, integrating techniques such as adversarial training and robust anomaly detection can enhance resilience. That adaptive algorithms can enhance the resilience and reliability of cybersecurity systems, advocating for future integration with technologies like blockchain and edge computing to address scalability and latency issues. These advancements pave the way for more robust and proactive cybersecurity defenses in an increasingly interconnected digital landscape.
Co-Authors Bennet, Daniel Bimantara, Ade Arya Fletcher, Eamon Mahyuni Maratis, Jerry Nurfaizi, Rifky Nurm, Sirje Pratomo, Rusdi Rahmania Az Zahra, Achani Ramadan, Ahmad Sudaryono Sudaryono Suhandi Suryari Purnama Victorianda