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Journal of Computer Science and Technology Application
Published by Pandawan Sejahtera Indonesia
ISSN : 30467616     EISSN : 30643597     DOI : https://doi.org/10.33050
Core Subject : Science, Education,
Computer Science & IT Other
The Journal of Computer Science and Technology Application (CORISINTA) is an international, open-access journal dedicated to advancing Information and Communication Technology (ICT). CORISINTA publishes research in Artificial Intelligence, Big Data, Cybersecurity, and Computer Networks. Through its rigorous double-blind peer-review process, the journal ensures the highest standards of quality. CORISINTA actively supports the United Nations Sustainable Development Goals (SDGs), including SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 17 (Partnerships for the Goals).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 41 Documents
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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.
Leveraging Blockchain Technology to Strengthen Cybersecurity in Financial Transactions: A Comprehensive Analysis Yusuf, David Arian; Wahyudin Anugrah, Rio; Arif Komara, Maulana; Julianingsih, Dwi; Garcia, Emily
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.v1i2.33

Abstract

In the rapidly evolving digital landscape, financial transactions are increasingly vulnerable to cyber threats, necessitating advanced security measures beyond traditional methods like encryption and firewalls. This study explores the potential of blockchain technology as a robust framework for enhancing cybersecurity protocols in financial transactions. The primary objective is to assess how blockchain’s decentralized, transparent, and cryptographic features can mitigate risks such as fraud, unauthorized access, and data breaches. Employing a quantitative experimental design, the study simulated financial transactions on a blockchain platform and analyzed historical data on security breaches. The results indicate that blockchain technology significantly improves data security, with a 98\% effectiveness rate in preventing and detecting breaches. However, challenges such as scalability, regulatory compliance, and high energy consumption were also identified. The findings suggest that while blockchain holds considerable promise for securing financial transactions, further innovation is necessary to address its limitations and fully leverage its capabilities in the financial sector.
Leveraging Big Data Analytics for Strategic Marketing Optimization: Insights and Impacts Fazri, Muhammad Faizal; Ramadhan, Tarisya; Apriliasari, Dwi; Julianingsih, Dwi; Fitzroy, Arabella
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.v1i2.39

Abstract

In the digital era, Big Data Analytics has emerged as a crucial tool for optimizing marketing strategies. This research explores the integration of Big Data into marketing, aiming to identify effective analytical techniques and their impact on marketing outcomes. The study utilized secondary data from various sources, including sales transactions, social media interactions, customer demographics, and web analytics. The analysis process involved data cleaning, integration, predictive modeling, clustering, sentiment analysis, and data visualization. The findings reveal that promotional campaigns and seasonal discounts significantly boost sales, with customer segmentation identifying three key groups: discount hunters, loyal customers, and occasional shoppers. Sentiment analysis shows positive customer feedback, though logistics-related issues warrant improvement. These results underscore the importance of targeted and personalized marketing strategies driven by data insights. The research contributes to marketing theories by providing empirical evidence on the effectiveness of Big Data Analytics in enhancing marketing strategies. Further research is recommended to explore its applicability across different industries, incorporate more diverse data sources, and utilize advanced analytical techniques to refine marketing strategies.
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.
Software-Defined Networking: Revolutionizing Network Management and Optimization Maulana, Sabda; Anjani, Sheila Aulia; Sanjaya, Yulia Putri Ayu; S, Sondang Visiana; Sithole, Precious
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/g1as4162

Abstract

This study investigates the impact of Software-Defined Networking (SDN) on network management and optimization, comparing its performance to traditional networking approaches. Through a mixed-methods approach, including empirical experiments and expert interviews, the research demonstrates that SDN significantly enhances network performance by reducing latency, increasing throughput, and providing superior security management. The scalability of SDN was also confirmed, with the network efficiently handling an increasing number of devices without performance degradation. However, the study identified challenges in integrating SDN into existing infrastructures and the need for specialized skills to manage SDN environments effectively. These findings underscore the potential of SDN as a transformative technology in modern network management, while also highlighting areas where further research is needed to address integration and skill-related challenges.
Big Data Analytics: Transforming Business Intelligence and Decision Making Usino, Wendi; Ayu Rini Kusumawardhani, Dhiyah; Ramadhan, Tarisya; Pratiangga, Aptanta; Qurotulain, Olivia
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/2wf1s376

Abstract

In today's rapidly evolving digital landscape, organizations are increasingly leveraging Big Data Analytics to transform business intelligence and enhance decision-making processes. This study explores how businesses utilize Big Data to gain insights into operations, customer behaviors, and market trends, specifically focusing on the retail, healthcare, and financial sectors. By employing a mixed-method approach that combines qualitative and quantitative data, the research analyzes case studies from a major international retailer, a leading healthcare provider, and a global bank. Data sources include semi-structured interviews with industry experts, surveys, and secondary data from existing literature. The findings indicate significant improvements in customer retention (20\%), operational efficiency (with a 15\% reduction in inventory costs in retail and a 10\% reduction in hospitalization rates in healthcare), and fraud reduction (a 25\% decrease in fraudulent transactions in financial services). However, the study also identifies ongoing challenges such as data quality issues, high implementation costs, and complexities in integrating Big Data Analytics with existing systems. The research concludes by emphasizing the importance of addressing these challenges to fully capitalize on Big Data's potential for competitive advantage and suggests that future studies should explore the ethical implications and the impact of emerging technologies on Big Data Analytics to further enhance its effectiveness in business intelligence.
Evaluating the Effectiveness of Machine Learning in Cyber Threat Detection Khanza, Aulia; Yulian, Firdaus Dwi; Khairunnisa, Novita; Yusuf, Natasya Aprila; Nuche, Asher
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ysdncf05

Abstract

In today's digital era, cyber threats pose significant challenges to organizations, necessitating more advanced detection methods. This study aims to evaluate the effectiveness of machine learning (ML) techniques in detecting cyber threats, focusing on supervised, unsupervised, and reinforcement learning models. Using datasets such as CICIDS2017, the study trains models including Random Forest, Support Vector Machines (SVM), and Neural Networks. The evaluation is based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that the Random Forest model outperforms others with an accuracy of 92.5\%, a precision of 91.8\%, and an F1-score of 92.4\%. This superior performance highlights its potential for real-time threat detection, as evidenced by a case study where the model effectively identified previously undetected cyber threats in a large technology company's network. However, the study also acknowledges challenges such as data quality and the need for continuous model updates. The findings suggest that integrating ML models into cybersecurity frameworks can significantly enhance threat detection efficiency. Future research should explore combining ML with traditional methods and improving model robustness against adversarial attacks to further advance cybersecurity measures.
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.
Big Data Analytics for Smart Cities: Optimizing Urban Traffic Management Using Real-Time Data Processing Miftah, Mohammad; Immaniar Desrianti, Dewi; Septiani, Nanda; Yadi Fauzi, Ahmad; Williams, Cole
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.v2i1.56

Abstract

Smart cities require efficient traffic management to address congestion and optimize urban mobility. With increasing urban populations and vehicle vol- umes, traditional traffic control systems struggle to meet growing demands, ne- cessitating advanced technological interventions. This study aims to explore the integration of big data analytics and real-time data processing in optimizing urban traffic management. By leveraging machine learning algorithms, sensor data, and predictive models, this research seeks to enhance traffic flow and improve overall transportation efficiency. The methodology involves col- lecting data from traffic sensors, GPS-equipped vehicles, and surveillance cameras, which are then analyzed using Apache Hadoop and Apache Spark to derive meaningful insights. Real-time data processing techniques ensure im- mediate responses to traffic conditions, dynamically adjusting signal timings and rerouting vehicles to mitigate congestion. The results indicate a 15-25% reduc- tion in travel times in high-traffic areas where real-time adaptive signal control is implemented. Furthermore, the analysis highlights distinct traffic patterns, congestion hotspots, and travel time optimization opportunities that can sig- nificantly enhance urban transportation efficiency. This research confirms that big data-driven traffic management can lead to better decision-making, im- proved commuter experiences, and reduced environmental impact through lower emissions. Future studies should focus on advanced predictive algo- rithms, connected vehicle technology, and AI-driven automation to further refine urban traffic solutions. By implementing real-time analytics, smart cities can develop sustainable, efficient, and adaptive traffic management systems that improve mobility and quality of life for urban residents.
Enhancing Network Security with Quantum Cryptography:A Study on Future-Proofing Computer Networks AgainstQuantum Attacks Supriati, Ruli; Purwanti; Anjani, Sheila Aulia; Anugrah, Rio Wahyudin; McCarthy, Ryan
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.v2i1.58

Abstract

The rapid development of quantum computing presents a significant challenge to existing cryptographic systems, such as RSA and Elliptic Curve Cryptography, which rely on the complexity of mathematical problems for security. Shor’s algorithm, which can efficiently solve these problems, emphasizes the need for cryptographic solutions that are resistant to quantum threats. This study aims to investigate the potential of Quantum Cryptography, with a specific focus on Quantum Key Distribution (QKD), to strengthen network security in response to emerging quantum computing risks. Despite the theoretical potential of QKD, there remains a gap in its practical application, particularly in terms of scalability, high implementation costs, and sensitivity to environmental factors, which have hindered its widespread adoption. The novelty of this research lies in the comprehensive approach it takes, combining theoretical analysis of QKD protocols, simulations using Qiskit, and comparisons with traditional cryptographic methods. This provides a more robust understanding of QKD effectiveness in different network scenarios. The study reveals that the BB84 protocol consistently outperforms the E91 protocol in terms of key generation efficiency and noise resilience. However, despite its unmatched security capabilities, QKD faces challenges such as scalability and implementation costs. To overcome these challenges and achieve widespread adoption, integrating QKD with post quantum cryptography and developing hybrid approaches are essential. Quantum Cryptography, particularly QKD, holds the potential to become a cornerstone for securing critical infrastructure, ensuring communication security in the quantum era.

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