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All Journal International Journal of Cyber and IT Service Management (IJCITSM) Journal of Computer Science and Technology Application
Nesti Anggraini Santoso
University of Raharja
Computer Science & IT Decision Sciences, Operations Research & Management Other

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Enhancing Predictive Models in System Development Using Machine Learning Algorithms Muhammad Hatta; Wahyu Nur Wahid; Faisal Yusuf; Farhan Hidayat; Nesti Anggraini Santoso; Qurotul Aini
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.159

Abstract

Predictive models play a crucial role in system development, enabling more informed decision making and improving system efficiency. However, traditional predictive models often struggle with scalability and accuracy in complex environments. This paper explores the use of Machine Learning (ML) algorithms to enhance predictive models, offering more accurate and scalable solutions. By leveraging key ML techniques such as decision trees, regression models, and neural networks, the study demonstrates how these algorithms can improve predictive accuracy and system performance. The methodology involves data collection, model training, and performance evaluation using various metrics to assess the effectiveness of ML enhanced predictive models. The results indicate a significant improvement in model accuracy and scalability, making ML a valuable tool in advancing system development processes. By incorporating ML frameworks specifically tailored to the unique demands of system development, this research offers new methodological adaptations designed to optimize scalability and performance. This study diverges from previous research by implementing and tailoring ML techniques uniquely suited for complex system development environments, enhancing both predictive accuracy and scalability.
Enhancing Predictive Models in System Development Using Machine Learning Algorithms Muhammad Hatta; Wahyu Nur Wahid; Faisal Yusuf; Farhan Hidayat; Nesti Anggraini Santoso; Qurotul Aini
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.159

Abstract

Predictive models play a crucial role in system development, enabling more informed decision making and improving system efficiency. However, traditional predictive models often struggle with scalability and accuracy in complex environments. This paper explores the use of Machine Learning (ML) algorithms to enhance predictive models, offering more accurate and scalable solutions. By leveraging key ML techniques such as decision trees, regression models, and neural networks, the study demonstrates how these algorithms can improve predictive accuracy and system performance. The methodology involves data collection, model training, and performance evaluation using various metrics to assess the effectiveness of ML enhanced predictive models. The results indicate a significant improvement in model accuracy and scalability, making ML a valuable tool in advancing system development processes. By incorporating ML frameworks specifically tailored to the unique demands of system development, this research offers new methodological adaptations designed to optimize scalability and performance. This study diverges from previous research by implementing and tailoring ML techniques uniquely suited for complex system development environments, enhancing both predictive accuracy and scalability.
Exploration of the Impact of Social Media on Children's Learning Mechanisms Emily Smith; Nesti Anggraini Santoso; Musyfiq amrullah Ar Romdhoni; Nur Azizah; Eka Dian Astuti
CORISINTA Vol 1 No 1 (2024): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/v1n1.21

Abstract

In this study, we investigated the complex relationship between social media and children's learning processes during childhood. Although research on student responses to e-learning platforms in pediatric contexts continues to grow, there remains significant confusion regarding the factors that shape acceptance of e-learning conducted via social media applications. To fill this gap, our study analyzes the influence of social media practices, especially in the context of knowledge sharing, features, motivation, and social media use, on how children respond to e-learning systems. In this effort, we expanded the existing Technology Acceptance Model (MPT) to accommodate these factors. We also conducted a comprehensive survey involving 100 children and their parents as research participants. They participate by answering questionnaires, which provide valuable empirical data. Using the powerful SMART-PLS analysis technique, we perform a careful analysis of the extended model. The results of our empirical data analysis confirm that social media practices have a significant positive impact on Perceived Effectiveness (PU) and Perceived Ease of Use (PEOU) in the context of children's learning mechanisms. Our main findings emphasize the central role of PU and PEOU in influencing how children receive e-learning systems. In other words, this paper highlights the potential changes that social media practices can bring about in shaping the acceptance of e-learning systems, while contributing to our understanding of the interactions between social media, MPT factors, and e-learning acceptance in the context of educational programs children.
Co-Authors Eka Dian Astuti Emily Smith Faisal Yusuf Farhan Hidayat Muhammad Hatta Musyfiq amrullah Ar Romdhoni Nur Azizah Qurotul Aini Wahyu Nur Wahid