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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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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.
Cybersecurity Strategies for Preventing Ransomware Attacks in Cloud-Based Applications Ageng Setiani Rafika; Sora Baltasar; Adiwijaya, Alfri; Riza Chakim, Mochamad Heru; Stefano Rizky, Zhask
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.v2i2.77

Abstract

Ransomware attacks have become a significant threat to cloud-based applications, posing severe risks to organizations' data integrity, financial stability, and operational continuity. This paper explores the challenges of securing cloud environments against ransomware, focusing on vulnerabilities such as inadequate encryption, weak access controls, and multi-tenancy risks. Through an in-depth analysis, the paper identifies the most common types of ransomware targeting cloud applications, including file encryption and data exfiltration ransomware, and discusses the security weaknesses that facilitate these attacks. The paper further evaluates existing cybersecurity strategies, such as data encryption, multi-factor authentication (MFA), and continuous monitoring, highlighting their effectiveness in preventing ransomware attacks. Based on these findings, a comprehensive framework is proposed, combining technical solutions like strong encryption and AI-based threat detection with organizational practices such as regular employee training and backup solutions. The study also emphasizes the importance of collaboration between cloud service providers and organizations to enhance overall cloud security. By adopting a multi-layered approach and integrating emerging technologies, organizations can significantly improve their resilience against ransomware threats. This research contributes to the ongoing dialogue on cloud security by providing actionable recommendations for preventing ransomware attacks and safeguarding cloud-based applications from evolving cyber threats.
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.
Augmented Reality in Preschool Enhancing Storytelling and Cognitive Development Pasmawati, Yanti; Kunang, Yesi Novaria; Hatta, Muhammad; Parker, Jonathan; Ramadhan, Dwi Nur
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.v2i2.104

Abstract

Augmented Reality (AR) is a technology that enables the integration of digital elements into the real world, creating more immersive and interactive learning experiences. In a study conducted at a local kindergarten, traditional storytelling methods often caused children to lose focus, particularly when the stories lacked engaging visual elements. In contrast, by using AR, stories such as the adventure of a cat could be brought to life through interactive 3D animations, allowing children not only to listen but also to interact with the characters. This study aims to examine the effectiveness of AR in enhancing storytelling and supporting the cognitive development of young children. A mixed-method approach was employed, comparing two groups: a control group using traditional methods and an experimental group using an AR application. Quantitative data were collected through pre- and post-tests, while qualitative data were obtained from direct observations and interviews with teachers and parents. The results revealed that the experimental group recorded a 32.10\% increase in post-test scores, significantly higher than the 7.34% increase in the control group. Furthermore, AR improved children’s engagement, enthusiasm, and collaboration during storytelling sessions. In conclusion, AR demonstrates considerable potential in supporting early childhood education by creating more engaging and inclusive learning experiences, although challenges such as technology accessibility and the availability of appropriate content still need to be addressed.
AI and Big Data in Advancing Mathematical Literacy Cybersecurity’s Moderating Role Sumliyah; Wardono; Mariani, Scolastika; Budi Waluya, Stevanus; Pujiastuti, Emi; Ikhsan, Ramiro Santiago
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.v2i2.109

Abstract

In today’s rapidly evolving digital landscape, mathematical literacy has emerged as a crucial competency for students navigating data-intensive environments. The integration of Artificial Intelligence (AI) and Big Data in education holds transformative potential to enhance personalized learning and support data-driven teaching strategies, yet it also raises critical concerns around Cybersecurity, particularly in safeguarding student data and ensuring trust in digital platforms. This study aims to analyze the effects of AI and Big Data on mathematical literacy, while examining the moderating role of Cybersecurity. Using a quantitative research approach, data were collected through a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 3. The results indicate that both AI and Big Data have significant positive effects on students’ mathematical literacy, with Big Data exerting the strongest influence through its ability to provide deep insights into student performance. AI also contributes effectively by enabling real-time feedback, adaptive learning, and personalized instruction. Although Cybersecurity demonstrated a weaker direct effect on mathematical literacy, it remains an essential enabler of a secure digital learning environment, fostering user trust and system integrity. This research highlights the importance of aligning educational technology implementation with strong digital safeguards to maximize learning outcomes. The findings offer managerial implications for educational institutions to invest in intelligent learning platforms supported by robust cybersecurity protocols. Ultimately, the study reinforces the relevance of SDG 4: Quality Education, by promoting inclusive, safe, and tech-enhanced learning ecosystems suited for the demands of 21st-century education.  

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