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Elfindah Princes
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INDONESIA
Journal of Mutidisciplinary Issues
Published by APPS Publications
ISSN : -     EISSN : 27986454     DOI : https://doi.org/10.53748/jmis.v1i1
Core Subject : Science, Agriculture, Social, Engineering,
Agriculture, Biological Sciences & Forestry Computer Science & IT Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science
Journal of Multidisciplinary (JMIS), Focus and Scope is Information Technology, Psychology, Environmental Science, Data Science, Language and Linguistics, Education, Data Sensor and Networking, Information System, Gamification, Health Science. JMIS is published frequency quarterly (May, August, November, and February) by APPS Publications.` This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. Every manuscript submitted to JMIS will not have any Article Processing Charges and Article Submission Charges. This includes submitting, peer-reviewing, editing, publishing, maintaining and archiving, and allows immediate access to the full-text versions of the articles.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)" : 6 Documents clear
The Impact of Online Platforms on Generation Z's Learning Styles and Educational Outcomes: A Comprehensive Study Elfindah Princes
Journal of Multidisciplinary Issues Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v2i2.50

Abstract

Purposes - This study aims to investigate the relationship between material comprehension and satisfaction among Generation Z learners using online platforms. Specifically, it examines how variations in material comprehension influence overall satisfaction with the learning experience. The research also explores possible indirect effects of other variables, such as motivation, on this relationship. Methodology - The study employed a quantitative research design, collecting data from 51 respondents. A path coefficient analysis was conducted to evaluate the direct and indirect effects of material comprehension on satisfaction. The data were analyzed using bootstrapping methods to determine the significance of the relationships between the variables. Findings - The analysis revealed that material comprehension significantly impacts satisfaction (p-value = 0.002), with higher levels of comprehension leading to greater satisfaction. Additionally, learning motivation was found to have both direct and indirect effects on satisfaction through its influence on material comprehension. However, some anomalies were observed where high comprehension did not always correlate with high satisfaction, indicating the influence of other factors. Novelty - This research contributes to the growing body of literature on Generation Z learning by focusing on the relationship between comprehension and satisfaction in online learning environments. The study also highlights the importance of understanding the role of motivation in enhancing both comprehension and satisfaction, offering new insights into the dynamics of digital education. Research Implications - The findings have important implications for educators and policymakers, suggesting that efforts to improve material comprehension can significantly enhance student satisfaction in online learning environments. Moreover, the study highlights the need to address motivational factors to maximize learning outcomes. Future research should explore additional variables that may mediate or moderate the relationship between comprehension and satisfaction.    
Navigating the Challenges of AI-Generated Content: Examining Public Trust, Accuracy, and Ethical Implications Elfindah Princes
Journal of Multidisciplinary Issues Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v2i2.54

Abstract

Purposes - The purpose of this research is to analyze the impact of the growth of AI-generated content on the accuracy and reliability of online information. Specifically, the research examines the challenges in detecting AI content, considering the limitations of AI tools like ZeroGPT and OpenAI’s Text Classifier, and explores how these challenges may influence public trust in online information. Methodology - This study employs a mixed-method approach combining quantitative data collection through surveys and qualitative case study analysis of AI-generated content controversies, such as articles from CNET and Microsoft. Data was analyzed using Structural Equation Modeling (SEM) to evaluate the relationships between AI usage and user trust. Findings - The results indicate that while there is a positive relationship between AI usage and public trust, the impact is not statistically significant. Issues like model collapse and AI inbreeding contribute to the challenge of maintaining content accuracy, which in turn affects the trustworthiness of AI-generated information. Novelty - This research contributes to the growing body of knowledge on AI-generated content by focusing on its impact on public trust, a relatively underexplored area. The study also introduces the concept of "model collapse" and "AI inbreeding" as critical factors that may undermine the reliability of AI-generated information. Research Implications - The findings have practical implications for media industries and AI developers. Enhancing AI algorithms to improve content accuracy and reliability, combined with stronger human oversight, could help mitigate the risks associated with AI-generated content and restore public trust in online information. The study also calls for the development of more advanced detection tools and ethical guidelines to govern the use of AI in information dissemination.
Impact of AI-Generated Content on AI Technology: Exploring Model Collapse and Its Implications Elfindah Princes
Journal of Multidisciplinary Issues Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v2i2.55

Abstract

Purposes - This research aims to investigate the phenomenon of "model collapse" within Generative Adversarial Networks (GANs) when AI models are trained using AI-generated content. The study focuses on understanding the implications of model collapse on the quality of AI outputs, exploring new concepts like "Model Autography Disorder" (MAD) and "Habsburg AI," and discussing the broader ethical and social impacts of AI self-consumption. Methodology - The study utilizes a mixed-methods approach, combining simulation experiments with qualitative interviews. GAN models were trained on AI-generated data to simulate model collapse, and various techniques were applied to mitigate this collapse. Expert interviews provided insights into the ethical considerations and future directions for generative AI development. Findings - The research demonstrates that model collapse significantly impacts the performance and diversity of AI outputs when trained on synthetic data. Although some mitigation techniques show potential, they do not fully prevent the collapse. Concepts like MAD and Habsburg AI offer deeper understanding into the risks of AI self-consumption and its broader implications for AI-driven systems. Novelty - The introduction of new terms like "Model Autography Disorder" and "Habsburg AI" adds unique perspectives to the discourse on AI sustainability. The study is among the first to examine the ethical and technical challenges posed by AI self-consumption and its long-term effects on AI-generated content. Research Implications - This study underscores the necessity for stricter guidelines on using AI-generated content in training models to prevent model collapse. It also highlights the need for hybrid training methods and ongoing ethical considerations to ensure the quality, reliability, and sustainability of AI-driven systems.      
Executive Summary Princes, Elfindah
Journal of Multidisciplinary Issues Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v4i1.64

Abstract

As the Chief Editor of the Journal of Multidisciplinary Issues, I am honored to present this special edition that explores the profound impacts of emerging technologies, particularly artificial intelligence (AI), on various aspects of society, education, and consumer behavior. This issue features a diverse collection of research papers that provide critical insights into the challenges and opportunities presented by AI and digital platforms. In "Impact of AI-Generated Content on AI Technology: Exploring Model Collapse and Its Implications," Elfindah Princes investigates the phenomenon of "model collapse" within Generative Adversarial Networks (GANs) when trained using AI-generated content. The study introduces new concepts such as "Model Autography Disorder" (MAD) and "Habsburg AI," shedding light on the risks of AI self-consumption and its broader implications for AI-driven systems. The research underscores the necessity for stricter guidelines on using AI-generated content in training models to prevent quality degradation and ensure the sustainability of AI systems​ Elfindah Princes also contributes to this edition with her research on "The Impact of Online Platforms on Generation Z's Learning Styles and Educational Outcomes." This study examines the relationship between material comprehension and satisfaction among Generation Z learners using online platforms. The findings reveal that higher levels of material comprehension lead to greater satisfaction, emphasizing the importance of motivation in enhancing both comprehension and satisfaction. The study highlights the need for educators to focus on designing online learning experiences that foster active engagement and critical thinking​ In another significant contribution, Elfindah Princes and Suppanunta Romprasert explore the challenges posed by AI-generated content in "Navigating the Challenges of AI-Generated Content: Examining Public Trust, Accuracy, and Ethical Implications." The study reveals a nuanced relationship between AI usage and public trust, noting that while AI can influence trust, its impact is currently limited by issues such as model collapse and the accuracy of AI-generated information. The research calls for stronger human oversight, transparency, and ethical guidelines to enhance the reliability of AI content and restore public trust in online information​ Collectively, the studies in this edition highlight the critical need for a balanced approach to integrating AI and digital technologies into various sectors. They underscore the importance of maintaining ethical standards, enhancing transparency, and ensuring human oversight to mitigate potential risks and maximize the benefits of these technologies. As AI continues to evolve, these insights will be invaluable for guiding its responsible and sustainable development.
Exploring Gen-Z Learning Preferences: A Comparative Study of Traditional, Online, and Blended Learning Models Princes, Elfindah; Soeryanto, Novianti; Romprasert, Suppanunta
Journal of Multidisciplinary Issues Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v4i1.65

Abstract

Purposes - The primary objectives of this research are to explore Gen-Z’s preferred learning environments, identify the factors influencing their choices, and uncover the challenges and opportunities associated with each learning model. Additionally, the study aims to provide actionable insights for educational policy-making and practice. Methodology - A quantitative research approach was employed, utilizing surveys distributed to a diverse sample of Gen-Z students aged 18-24 currently enrolled in higher education. The survey collected data on participants' preferences, engagement levels, and the effectiveness of different learning models. Statistical analyses were performed to assess the relationships between the variables. Findings - The findings reveal that Gen-Z shows a strong preference for online and blended learning models over traditional classroom settings. The study highlights the significant impact of elements such as connectivism and constructivism on learning model effectiveness, while factors like student engagement and participant information also play moderate roles. However, the direct influence of knowledge acquisition on the choice of learning model was found to be minimal. Novelty - This research contributes to the limited academic literature on Gen-Z learning preferences by focusing on the comparative effectiveness of different educational models. The study provides a contemporary understanding of how digital natives interact with learning environments, offering insights that are crucial for developing future educational strategies. Research Implications - The study’s results have practical implications for educators and policymakers. By aligning teaching methods with Gen-Z’s preferences, educational institutions can enhance student engagement and learning outcomes. Furthermore, the research underscores the need for integrating technology into education and preparing for future shifts in learning trends among younger generations.
A Study on the Association of Purchase Behavior and Social Media of Gen Z in Indonesia Putra, Akira Wahyu
Journal of Multidisciplinary Issues Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v4i1.66

Abstract

Purposes: This research aims to explore the association between social media usage and purchase behavior among Generation Z in Indonesia. Specifically, it seeks to understand how social media content and influencers impact the purchasing decisions of this demographic, providing insights that can inform digital marketing strategies tailored to Gen Z. Methodology: The study employs a quantitative research approach, utilizing primary data collected through online surveys. Purposive sampling was used, targeting individuals from Generation Z with experience using e-commerce platforms and active social media accounts. Data analysis was conducted using Smart Partial Least Square (SmartPLS) and Structural Equation Modeling (SEM) to test the proposed hypotheses. Findings: The research findings support the hypotheses that social media content and influencers significantly influence the purchase behavior of Indonesian Gen Z. The study reveals that engaging and visually appealing social media content, along with influencer endorsements, plays a crucial role in shaping the purchasing decisions of this demographic. Novelty: This research contributes to the existing literature by providing a focused analysis of the Indonesian Gen Z population, a demographic that has not been extensively studied in this context. The study also offers practical insights for businesses and policymakers aiming to enhance digital marketing strategies and e-commerce platforms for this specific audience. Research Implications: The study suggests that businesses should prioritize creating compelling social media content and leveraging influencer marketing to effectively reach and engage Gen Z consumers in Indonesia. Additionally, it highlights the need for improved ICT infrastructure to support the growing e-commerce landscape in the country.

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