Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Cyber and IT Service Management (IJCITSM) Journal of Computer Science and Technology Application
Muhammad Hatta
Catur Insan Cendekia University
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.
Augmented Reality in Preschool Enhancing Storytelling and Cognitive Development Yanti Pasmawati; Yesi Novaria Kunang; Muhammad Hatta; Jonathan Parker; Dwi Nur Ramadhan
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.
Co-Authors Dwi Nur Ramadhan Faisal Yusuf Farhan Hidayat Jonathan Parker Nesti Anggraini Santoso Qurotul Aini Wahyu Nur Wahid Yanti Pasmawati Yesi Novaria Kunang