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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 10 Documents
 1 
Search results for , issue "Vol 1 No 2 (2024): August" : 10 Documents clear
Revolutionizing Logistics Business Models through Big Data and Blockchain: A Business Model Canvas Analysis Ainun Mutiara, Indah; Febriansyah, Yusuf; Kamal, Mustofa; Zainum Ikhsan, Ramzi; Williams, Tane
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.v1i2.28

Abstract

The world is moving quickly towards automation and digitalization in the modern era. This change is becoming crucial to corporate competitive strategies, especially in the logistics industry. The use of data in organizational decision-making is an essential aspect of this digital and automated environment. Several business sectors are implementing Big Data and Blockchain technologies to improve organizational capabilities by developing effective business processes. This inexorably affects the development of new business models that fit the changing global business landscape. The Business Model Canvas (BMC) is an effective tool for analyzing internal and external business model changes. A SWOT analysis of these business model transformations is necessary to explain the new business process changes further. First, the analysis shows that for businesses to function at their best, current technological advancements—particularly in Big Data and Blockchain—will continue to disrupt them. Second, there have been significant internal and external changes to intra- and inter-organizational relationships due to the implementation of Big Data and Blockchain. Thirdly, the benefits of Blockchain and Big Data technologies for business, especially logistics, can be further explained by SWOT analysis.
Optimizing Digital Marketing Strategies through Big Data and Machine Learning: Insights and Applications Andayani, Dwi; Madani, Muchlishina; Agustian, Harry; Septiani, Nanda; Wei Ming, LI
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.v1i2.29

Abstract

In the dynamic realm of digital marketing, the convergence of Big Data and machine learning has ushered in transformative changes, reshaping strategies through advanced data analytics and predictive modeling. This paper examines the pivotal role of these technologies in enhancing marketing practices, focusing on their impact on consumer targeting, engagement, and overall campaign effectiveness. By harnessing vast datasets and applying sophisticated machine learning algorithms, marketers can now predict consumer behavior with unprecedented accuracy, personalize marketing messages, and optimize operational strategies to maximize engagement and return on investment. Despite the profound advantages, the integration of these technologies raises substantial challenges, including data privacy concerns and the need for specialized skills. Through a mixed-methods approach combining quantitative data analysis and qualitative interviews, this study not only demonstrates the improved predictive accuracy and segmentation capabilities afforded by these technologies but also discusses the barriers to their full potential realization. The findings highlight a clear trajectory towards more data-driven, responsive marketing paradigms, suggesting a future where digital marketing strategies are increasingly informed by insights derived from Big Data and machine learning. This paper aims to provide a comprehensive overview of the current landscape and future potential of these transformative technologies in digital marketing.
Enhancing Cybersecurity Risk Management Strategies in Financial Institutions: A Comprehensive Analysis of Threats and Mitigation Approaches Kristian, Agus; Az-Zahra, Achani Rahmania; Hidayat, Farhan; Yadi Fauzi, Ahmad; Kallas, Evelin
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.v1i2.31

Abstract

This study investigates the cybersecurity risks faced by financial institutions, with a particular focus on identifying common threats, evaluating their impact, and assessing the effectiveness of risk management strategies. Utilizing a mixed-methods approach, data were collected from both primary and secondary sources, including expert interviews, surveys, and a review of academic and industry literature. The results highlight that phishing, ransomware, and malware are among the most prevalent threats, with email and websites being the primary attack vectors. The study also examines the significant financial and reputational impacts these threats pose. A case study of XYZ Bank demonstrates how a layered approach to cybersecurity, involving prevention, detection, response, and recovery strategies, can substantially reduce the frequency of cyber incidents. The findings emphasize the importance of continuous updates to security policies, regular employee training, and investment in advanced security technologies. The study concludes with recommendations for financial institutions to enhance their cybersecurity posture through comprehensive risk management 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.
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.

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