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All Journal Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Journal of Computer Science and Technology Application
Rizky, Agung
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : Journal of Computer Science and Technology Application

 1 
Advanced Cyber Threat Detection: Big Data-Driven AI Solutions in Complex Networks Rizky, Agung; Zaki Firli, Muhammad; Aulia Lindzani, Nur; Audiah, Sipah; Pasha, Lukita
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.v1i2.42

Abstract

In the rapidly evolving digital landscape, cybersecurity has become increasingly critical, especially within complex network environments. This research presents the development of a cyber threat detection system that leverages Artificial Intelligence (AI) and Big Data analytics to enhance accuracy and speed in identifying and responding to cyber threats. The system was evaluated through rigorous testing, demonstrating a high detection accuracy of 95\% for malware and unauthorized access attempts, along with an impressive detection speed of 2 seconds on average for most threats. Additionally, the system exhibited strong scalability, maintaining optimal performance even with increasing network complexity. These findings underscore the system's robustness and practical applicability in real-world scenarios. However, further refinement is suggested to improve anomaly detection and reduce response times for more complex threats. This study contributes valuable insights into the integration of AI and Big Data in cybersecurity, providing a scalable and effective solution for protecting critical network infrastructures.
Optimization of Machine Learning Algorithms for Fraud Detection in E-Payment Systems Rizky, Agung; Gunawan, Ahmad; Komara, Maulana Arif; Madani, Muchlisina; Harris, Ethan
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.v2i1.68

Abstract

This study explores the optimization of machine learning algorithms for fraud detection in electronic payment (e-payment) systems. The rapid growth of e-payment platforms has introduced significant challenges in ensuring the security and integrity of financial transactions. Fraud detection plays a pivotal role in mitigating these risks, and the application of machine learning (ML) has emerged as a powerful tool to identify fraudulent activities. This research examines how Data Quality (DQ), Algorithm Selection (AS), and Optimization Techniques (OT) influence Model Performance (MP) and, subsequently, Fraud Detection Effectiveness (FDE). The study utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 3 to analyze the relationships between these variables. The results demonstrate that high Data Quality significantly enhances Model Performance, while Algorithm Selection and Optimization Techniques also contribute positively, albeit to a lesser extent. The findings reveal that Model Performance plays a crucial mediating role between these factors and the effectiveness of fraud detection. Fraud Detection Effectiveness is found to be significantly impacted by Model Performance, suggesting that improving model accuracy and efficiency is essential for better fraud detection outcomes. Reliability and validity tests show strong internal consistency for all constructs, with Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE) all reaching satisfactory levels. The study highlights the importance of data preprocessing, the careful selection of machine learning models, and optimization techniques in achieving high-performing fraud detection systems. The results provide valuable insights for the development of more robust and scalable fraud detection mechanisms in e-payment systems, contributing to the broader field of machine learning and cybersecurity. Future research could explore advanced techniques like deep learning and blockchain integration for further enhancement of fraud detection systems.
Harnessing AI to Improve Operational Effectiveness and Strengthen Organizational Adaptability Rizky, Agung; Arifin, Ridwan; Arif Andika; Maria Daeli, Ora Plane; Hua, Chua Toh
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.v2i2.129

Abstract

This study explores the dual role of Artificial Intelligence (AI) in improving operational effectiveness and fostering organizational agility, two critical factors for success in today’s dynamic business environment. By leveraging technologies such as machine learning, predictive analytics, and robotic process automation, organizations can streamline workflows, enhance cost efficiency, and enable data-driven decision-making. The research adopts a qualitative approach, analyzing case studies and expert insights to uncover key findings. Results indicate that AI implementation significantly enhances process speed, decision accuracy, and adaptability while reducing operational costs. However, challenges such as resistance to change, high implementation costs, and ethical concerns—particularly regarding data privacy—pose barriers to adoption. To address these, organizations must adopt strategic measures such as phased implementation, robust training programs, and ethical frameworks. The study introduces a conceptual model that illustrates AI's central role in driving efficiency and adaptability, supported by comparative performance metrics demonstrating tangible benefits. This research contributes to the broader understanding of AI’s transformative impact, emphasizing its potential as a catalyst for innovation and competitiveness. Furthermore, it provides practical recommendations for overcoming barriers to adoption, ensuring sustainable integration of AI technologies. By addressing both opportunities and challenges, the findings serve as a roadmap for organizations aiming to harness AI's full potential. Future research should focus on industry-specific applications and strategies to tailor AI adoption to unique organizational needs, thereby maximizing its impact across diverse sectors. This study concludes that AI is indispensable for organizations striving to thrive in a rapidly evolving digital landscape.
Co-Authors Amsyar, Izwan Arif Andika Audiah, Sipah Aulia Lindzani, Nur Bagong Suyanto Beldiq, Eiser Aaron Cristhopher, Ethan Dyatmika, Sutama Wisnu Ekawaty, Anita Gunawan, Ahmad Harris, Ethan Hua, Chua Toh Komara, Maulana Arif Madani, Muchlisina Maria Daeli, Ora Plane Mkhize, Thabo Mochamad Heru Riza Chakim Ndlovu, Zinhle Ninda Lutfiani Pasha, Lukita Ramadan, Ahmad Ridwan Arifin Rodriguez, Marta Santiago Ikhsan, Ramiro Setijaningrum, Erna Sumliyah, Sumliyah Tanjung, Azrul Tantri Wenny Sitanggang Untung Rahardja Utami, Rahayu Tri Yul Ifda Tanjung, Yul Ifda Zaki Firli, Muhammad