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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 2 No 1 (2025): February" : 10 Documents clear
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.
AI-Driven Big Data Solutions for Personalized Healthcare: Analyzing Patient Data to Improve Treatment Outcomes Rafika, Ageng Setiani; Faturahman, Adam; Henry, Bintang Nandana; Yulian, Firdaus Dwi; Hassan, Mohammed
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.v2i1.61

Abstract

The advent of AI-driven big data solutions has transformed personalized healthcare by enabling the analysis of vast and complex patient datasets to optimize treatment outcomes. This study aims to evaluate the effectiveness of AI models in improving healthcare delivery through enhanced diagnostic accuracy, reduced processing times, and personalized treatment plans. The research utilizes AI models to process extensive patient data from electronic health records, wearable devices, and genetic information. The results show an impressive accuracy rate of 93%, a 25% reduction in diagnostic errors, and significant improvements in patient outcomes, including 72% of patients receiving more accurate diagnoses and 65% experiencing faster recovery. A comparison with traditional methods highlights the advantages of AI in scalability, efficiency, and reliability, offering a clear improvement over existing healthcare approaches. However, challenges such as data bias, ethical concerns, and scalability need to be addressed to en- sure the responsible application of AI in healthcare systems. In conclusion, this research provides valuable insights for healthcare organizations that aim to implement AI-driven solutions, fostering the advancement of patient care and encouraging innovation in the industry. The findings suggest that AI-powered big data solutions have the potential to revolutionize healthcare, improving diagnostic precision and treatment personalization, ultimately enhancing patient satisfaction and outcomes.
Cybersecurity in the Age of IoT and Developing Frameworks for Securing Smart Devices and Networks Rahayu, Eli Ratih; Aprillia, Ariesya; Ikhsan, Ramzi Zainum; Adiwijaya, Alfri; Kumara, Aryan
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.
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.
The Role of Natural Language Processing in Enhancing Chatbot Effectiveness for E-Government Services Siahaan, Mungkap Mangapul; Sunarjo, Richard Andre; Sebastian, Rizky; Wahid, Syahrul Muarif
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.v2i1.71

Abstract

The rapid digital transformation of public administration has led to the adoption of (B) Natural Language Processing (NLP)-powered chatbots to enhance the accessibility, efficiency, and responsiveness of (O) e-government services. However, despite their increasing deployment, many government chatbots still struggle with intent recognition, response accuracy, multilingual processing, and user engagement, limiting their effectiveness. This study investigates (M) the role of NLP in improving chatbot performance within e-government services by evaluating four case studies: Ask Jamie (Singapore), UK Government Digital Assistant, MyGov Corona Helpdesk (India), and Gov.sg Chatbot. Using a mixed-methods approach, this research assesses chatbot effectiveness based on accuracy, response time, query resolution rate, and user satisfaction metrics. The findings indicate that (R) NLP-driven chatbots significantly outperform rule-based systems, with higher accuracy (up to 89%), faster response times (~2.1 seconds), and improved query resolution rates (92%), demonstrating their capacity to automate public service delivery efficiently. However, key challenges remain, including bias in NLP models, data privacy concerns, and the difficulty of integrating NLP chatbots into legacy IT infrastructures. Additionally, multilingual processing remains a limitation, affecting inclusivity for diverse populations. To overcome these challenges, this study proposes advancements in adaptive NLP models, real-time learning algorithms, ethical AI frameworks, and blockchain-based security solutions to ensure fair, secure, and transparent chatbot interactions in digital governance. These findings contribute to the growing body of research on AI-driven public service automation and highlight the potential of NLP to enhance (C) citizen-government interactions, reduce administrative burdens, and improve trust in e-government platforms. Future research should focus on bias mitigation, improving multilingual NLP capabilities, and integrating AI ethics into chatbot governance frameworks to ensure sustainable, scalable, and citizen-centric e-government chatbot solutions.
Challenges and Opportunities in Implementing Big Data for Small and Medium Enterprises (SMEs) Cahyono, Dwi; Sijabat, Apriani; Panjaitan, Muktar Bahruddin; Julianingsih, Dwi; Lorenzo, Agung
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.v2i1.74

Abstract

Small and Medium Enterprises (SMEs) play a crucial role in the global economy but often face significant challenges when adopting new technologies like Big Data. While Big Data offers opportunities for improving decision-making, operational efficiency, and gaining a competitive edge, many SMEs struggle due to financial constraints, limited technical expertise, and concerns over data security and privacy. This paper explores the challenges SMEs encounter in adopting Big Data and identifies the opportunities it provides for growth and innovation. A mixed-methods approach is employed, combining qualitative interviews with SME managers and quantitative surveys from 150 SMEs to gather comprehensive data. The findings reveal that SMEs face barriers such as high implementation costs and lack of skilled personnel, but they also recognize the potential for Big Data to enhance customer insights, improve business processes, and foster new business models. Recommendations include exploring cost-effective solutions, investing in employee training, strengthening data security, and adopting modular systems that integrate easily with existing operations. This study underscores the importance of overcoming these challenges and leveraging Big Data as a key driver of digital transformation for SMEs, ultimately helping them to compete more effectively in an increasingly data-driven marketplace.
Transforming Human Resource Practices in the Digital Age: A Study on Workforce Resilience and Innovation Setiawan, Sandy; Rusilowati, Umi; Jaya, Aswadi; Hetilaniar; Wang, Rion
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.v2i1.80

Abstract

The rapid advancement of digital technologies has significantly transformed human resource (HR) practices, influencing workforce resilience and organizational innovation. As organizations navigate evolving work environments, the integration of technology-driven HR strategies has become essential for maintaining competitiveness. Traditional HR models are being replaced by more automated and data-driven systems, shaping the future of workforce management. This study aims to examine the intersection of HR practices and digital transformation, with a particular focus on how digital tools enhance workforce resilience and foster organizational innovation. It explores the role of AI-driven talent management systems, data-driven decision-making, and adaptive HR strategies in optimizing recruitment, performance evaluation, and employee engagement. A mixed-method approach was employed, combining qualitative and quantitative analyses. Data was collected through a systematic literature review, multiple case studies, and in-depth interviews with HR professionals across various industries. These methods provided comprehensive insights into the evolving landscape of digital HR practices. The findings highlight the critical role of continuous learning, agile work structures, and active employee engagement in fostering a resilient workforce. The adoption of AI-powered HR tools has proven effective in improving decision-making, employee retention, and performance management, ultimately leading to greater organizational adaptability and innovation. This study concludes that digital transformation in HR is not merely an operational shift but a strategic necessity. By successfully integrating digital tools, businesses can create a more flexible, agile, and responsive work environment, fostering long-term growth and sustainability in an increasingly competitive market.
The Influence of E-Commerce and Digital Marketing on Startupreneur Performance Using PLS-SEM Meria, Lista; Andriyansah; Priandito; Lutfiani, Ninda; Awhina, Ridan Ahsani Te
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.v2i1.82

Abstract

Startupreneurs increasingly rely on technology to manage their business, especially in using e-commerce applications and digital business marketing strategies. This research investigates the influence of e-commerce applications and digital business marketing on startup entrepreneur performance. The research method used is an online survey of 150 startup entrepreneurs in Indonesia who are active in various industries. Data was collected using a questionnaire that measures the level of use of e-commerce applications, digital business marketing practices, and startupreneur performance based on factors such as revenue growth, customer satisfaction, and market visibility. The results of the analysis show that the use of e-commerce applications has a significant favourable influence on startupreneur performance. Startupreneurs who actively use e-commerce applications tend to have higher revenue growth and better levels of customer satisfaction. Additionally, digital business marketing strategies were also found to positively affect startup business performance, especially in increasing market visibility and broader market penetration. This research makes an essential contribution to understanding how technology and digital marketing strategies can help improve startupreneur performance. The practical implication of this research is the importance of integrating e-commerce applications and digital business marketing in startup business growth strategies. Steps to improve startupreneur performance could involve investing in digital technology and improving skills in managing online marketing strategies.

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