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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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Empowering Edupreneurship through AI-Based Creative Journalism Education Hayatun Nufus; Subyantoro; Hari Bakti Mardikantoro; Rahayu Pristiwati; Zainarthur, Henry
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.v2i2.112

Abstract

The advancement of Artificial Intelligence (AI) has reshaped journalism and education, creating opportunities to strengthen creative writing and entrepreneurial skills. AI-driven journalism platforms, such as automated content generation and real-time feedback, empower students to become both skilled writers and edupreneurs, while developing 21st-century competencies including creativity, critical thinking, collaboration, and digital literacy. This study examines the influence of AI integration on creative writing skills, edupreneurship, and competency development using a quantitative research design. Data were collected through structured questionnaires distributed to university students and analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. The analysis revealed high reliability and convergent validity of the measurement model, with structural results confirming significant positive relationships between AI integration and creative writing skills, and between creative writing skills and 21st-century competencies. Furthermore, creative writing was shown to mediate the connection between AI adoption and both entrepreneurship and competency development, indicating its key role in linking technological tools with educational and entrepreneurial outcomes. The findings underline the transformative role of AI in journalism education, not only in enhancing writing proficiency but also in building entrepreneurial capacity and essential future-ready skills. This \textbf{research provides} practical implications for integrating AI literacy, creative expression, and entrepreneurship into curricula, while also aligning with Sustainable Development Goals (SDGs) such as Quality Education, Economic Growth, Innovation, and Global Partnerships, supporting inclusive and technology-driven learning.
Revolutionizing Renewable Energy Systems throughAdvanced Machine Learning Integration Approaches Sri Rahayu; Septiani, Nanda; Ramzi Zainum Ikhsan; Kareem, Yasir Mustafa; Untung Rahardja
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.v2i2.115

Abstract

The increasing global emphasis on sustainability has accelerated investments in renewable energy technologies, positioning sources like solar, wind, and hydroelectric power as vital alternatives to fossil fuels. Despite significant progress, integrating renewable energy into existing grids remains challenging due to variability in energy output, grid instability, and inefficiencies in energy storage systems. This study investigates the potential of machine learning (ML) to revolutionize the renewable energy sector by enhancing energy forecasting, grid management, and energy storage optimization. Using a combination of supervised learning, deep learning, and reinforcement learning techniques, we developed predictive and optimization models based on historical and real-time datasets. Additionally, structural equation modeling (SEM) with SmartPLS was employed to analyze the relationships between key variables, such as machine learning algorithms, renewable energy sources, sustainability performance, and operational efficiency. The results indicate that machine learning significantly improves energy forecasting accuracy, grid reliability, and storage efficiency, with R-squared values of 0.685 for operational efficiency and 0.588 for sustainability performance. These findings highlight the transformative role of ML in optimizing renewable energy systems and achieving sustainable energy goals. While ML offers promising solutions for renewable energy challenges, further research is needed to address real-time data integration, model scalability, and economic feasibility. This study provides a foundation for future innovations, emphasizing the importance of intelligent, data-driven strategies in advancing global energy sustainability.
Integrating AI and Big Data to Enhance Performance andSustainability in Hospitality Hasrul Azwar Hasibuan; Syaifuddin; Rusiadi; Edwards, John
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.v2i2.118

Abstract

This paper explores the impact of Big Data and Artificial Intelligence (AI) on Employee Performance and Sustainability in the hospitality industry. The paper further explains how integrating Big Data and AI can optimize operations, enhance employee efficiency, and promote sustainable practices. The research uses SmartPLS to analyze the relationships between these variables, with a focus on how Big Data and AI influence Employee Performance, which in turn contributes to Sustainability efforts. The findings, show that both Big Data and AI have significant positive effects on Employee Performance, with Big Data demonstrating a stronger impact. Moreover, Employee Performance mediates the relationship between Big Data, AI, and Sustainability, indicating that improvements in employee performance lead to better sustainability outcomes, such as resource optimization and waste reduction. The study findings align with SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production), highlighting the potential of technology to drive both economic and environmental sustainability in the hospitality sector This research contributes to understanding how the application of Big Data and AI can help hospitality businesses achieve long-term success through improved operational efficiency and sustainable practices.
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.
The Impact of Educational Information Systems on Learning Accessibility in Higher Education Sudadi Pranata; Arif Komara, Maulana; Amelia, Fhia; Rangi, Noah
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.v2i2.132

Abstract

This study explores the impact of educational information systems on enhancing learning accessibility in higher education, as digital tools increasingly become integral to academic support, and student engagement. The main objective is to assess how these systems improve access to learning resources and facilitate communication, particularly for students from diverse backgrounds and with varying educational needs. Using a mixed-methods approach, this research combines quantitative analysis of accessibility metrics with qualitative insights from surveys and interviews with students and faculty across different higher education institutions. The findings show that educational information systems significantly enhance learning accessibility by providing flexible access to resources, facilitating real-time feedback, and supporting personalized learning paths. These systems also improve student engagement by enabling convenient access to materials and fostering a collaborative learning environment that accommodates different learning styles. However, the study identifies several barriers, including gaps in digital literacy, usability challenges, and unequal access to the necessary infrastructure, which can limit the effectiveness of these systems in reaching all students equally. Additionally, concerns around data privacy and system complexity are noted as areas needing attention to build user trust and ensure smoother system integration. The study concludes that while educational information systems hold great promise for improving accessibility and inclusivity in higher education, addressing these barriers through targeted training, digital equity initiatives, and robust data protection policies is essential for maximizing their potential. These insights offer valuable guidance for educational institutions aiming to create more inclusive learning environments through strategic integration of educational information systems.
AI-Based Technopreneurship for Speech Disorder Therapyin ADHD Children Uki Hares Yulianti; Zulaeha, Ida; Subyantoro; Yusro Edy Nugroho; Moyo, Kgomotso
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.v2i2.135

Abstract

While existing literature explores AI in healthcare, this study uniquely high-lights technopreneurial approaches to address accessibility and affordability ofAI-based therapies. Focusing on Indonesia as a case study, the paper exam-ines how AI can support children with ADHD who often face delays in speechdevelopment and communication. ADHD affects attention, language process-ing, and social interaction, creating challenges for effective therapy. AI inte-grates machine learning to analyze speech patterns, detect phonological errors,and provide adaptive therapy exercises tailored to children’s developmental lev-els. Findings indicate that AI improves diagnostic accuracy and delivers engag-ing interventions through interactive and gamified tools, enhancing motivationand participation for children who struggle with conventional therapy. AI sys-tems can track progress and adjust feedback in real time, offering personalizedsupport. However, barriers remain regarding affordability, infrastructure, andthe need for human oversight to manage complex emotional and behavioral re-sponses. In this context, technopreneurship is essential to scale affordable AI-based therapies for schools, clinics, and homes. By bridging gaps in healthcaredelivery, this study contributes to SDG 3 (Good Health), SDG 4 (Quality Ed-ucation), and SDG 10 (Reduced Inequalities). It emphasizes the importance ofinterdisciplinary collaboration among AI developers, speech-language patholo-gists, medical professionals, and educators. Overall, AI-driven technopreneur-ship demonstrates strong potential to improve early detection, therapy person-alization, and language development for children with ADHD, while ensuringbroader accessibility and sustainability.
Optimizing Employee Performance and Sustainability with Big Data and AI in Hospitality Asti Veto Mortini; Sri Wuli Fitriati; Rahayu Puji Haryanti; Sri Wahyuni; Rodriguez, Marta
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.v2i2.138

Abstract

This study explores the impact of Big Data and Artificial Intelligence (AI) on Employee Performance and Sustainability in the hospitality industry. By integrating Big Data and AI, hospitality businesses can optimize operations, enhance employee efficiency, and promote sustainable practices. The research uses SmartPLS to analyze the relationships between these variables, with a focus on how Big Data and AI influence Employee Performance, which in turn contributes to Sustainability efforts. The results show that both Big Data and AI have significant positive effects on Employee Performance, with Big Data demonstrating a stronger impact. Moreover, Employee Performance mediates the relationship between Big Data, AI, and Sustainability, indicating that improvements in employee performance lead to better sustainability outcomes, such as resource optimization and waste reduction. The study’s findings align with SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production), highlighting the potential of technology to drive both economic and environmental sustainability in the hospitality sector. This research contributes to understanding how the application of Big Data and AI can help hospitality businesses achieve long-term success through improved operational efficiency and sustainable practices
Optimization of Digital Business to Support MSMEs Growth in the Industry 4.0 Transformation Jaya, Aswadi; Saputra, Farhan; Derlina; Ramadhan, Dwi Nur; Green, Thomas
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v3n1.147

Abstract

Digital transformation has become essential for Micro, Small, and Medium-Sized Enterprises (MSMEs) in the Industry 4.0 age in order to improve resilience and competitiveness in spite of restricted resources. The influence of digital optimization techniques on MSME growth is investigated in this study. These tactics include digital marketing adoption, e-commerce platform usage, and digital financial management tools. Data from 100 MSMEs was gathered quantitatively using structured questionnaires, and the correlations between the variables were examined using SmartPLS 3. E-commerce platform usage has the biggest impact, followed by digital financial management tools and digital marketing adoption, according to the results, which show that all digital strategies have a favorable impact on MSME growth. According to the model's R Square value of 0.694 for MSME Growth, the examined strategies account for around 69.4% of the growth variation. These results demonstrate how crucial it is for MSMEs to embrace digital technology in order to increase their market reach, boost operational effectiveness, and fortify financial management. Future research is urged to examine other factors impacting digital adoption and to apply these findings in a variety of sector scenarios. The study concludes that MSMEs must invest in digital skills in order to achieve sustainable development in a competitive digital world.
Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques Eryc; Nasib; Muh. Fahrurrozi; Ramzi Zainum Ikhsan; Parker, Jonathan
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/p8sbs746

Abstract

This study, titled Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques, explores how artificial intelligence (AI) particularly machine learning (ML) can enhance the accuracy and strategic impact of business forecasting in dynamic markets. Traditional statistical forecasting methods often fail to accommodate complex, nonlinear, and high-dimensional data. To address this gap, the research develops and validates a machine learning–based forecasting model designed to integrate predictive analytics into strategic decision-making. The study adopts a quantitative approach and employs Structural Equation Modeling (SEM) using SmartPLS 3 to examine the interrelationships among four latent variables: Market Trends (MT), Forecasting Accuracy (FA), Strategic Planning Efficiency (SPE), and Business Performance (BP). Each construct is measured using three indicators, forming a structural model that tests six hypothesized relationships. The results indicate that understanding market trends significantly improves forecasting accuracy and strategic planning efficiency, which in turn positively influences business performance. Furthermore, forecasting accuracy directly enhances both planning efficiency and overall performance, emphasizing the strategic value of data-driven insights. The findings validate the reliability and predictive power of the proposed model, offering a robust framework for organizations aiming to leverage machine learning in strategic forecasting. By bridging the gap between algorithmic prediction and managerial application, this study contributes to the growing field of AI-driven business analytics and supports the development of more agile, informed, and resilient business strategies in a data-centric economy.
A Framework for Mining Customer Data in Management Information Systems Untung Rahardja; Lutfiani, Ninda; Agung Rizky; Yul Ifda Tanjung; Evans, Richard
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/m5qymx32

Abstract

The exponential growth of customer data within Management Information Systems (MIS) has generated an urgent need for structured analytical approaches capable of transforming raw information into valuable insights that support decision-making across various organizational processes. This study aims to develop a comprehensive and systematic framework for mining customer data in MIS by integrating preprocessing procedures, machine learning algorithms, and model evaluation techniques into a unified analytical workflow. Using the Design Science Research methodology, the framework was designed based on existing data mining standards, developed through iterative refinement, and demonstrated using a customer-behavior dataset processed with clustering, classification, and association rule mining techniques. The findings reveal that the proposed framework improves data quality, enhances segmentation accuracy, and strengthens predictive capability, enabling MIS to deliver deeper insights into customer behavior, purchasing tendencies, and potential churn risks. Experimental results show that combining K-Means, Random Forest, and Apriori algorithms yields more comprehensive and reliable patterns compared to using a single analytical technique. The outcomes of this research highlight the practical significance of applying an integrated data mining approach in MIS, allowing organizations to optimize marketing strategies, personalize services, and make more informed managerial decisions. Overall, this study contributes to the field by offering a scalable, adaptable, and effective framework for implementing customer data mining within real-world MIS environments.

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