Enhancing Predictive Models in System Development Using Machine Learning Algorithms

Muhammad Hatta; Wahyu Nur Wahid; Faisal Yusuf; Farhan Hidayat; Nesti Anggraini Santoso; Qurotul Aini

doi:10.34306/ijcitsm.v4i2.159

Authors

Muhammad Hatta Catur Insan Cendekia University https://orcid.org/0000-0002-1892-9008
Wahyu Nur Wahid Seni Sejahtera Indonesia
Faisal Yusuf University of Semarang https://orcid.org/0009-0003-5054-7733
Farhan Hidayat Alfabet Inkubator Indonesia
Nesti Anggraini Santoso University of Raharja
Qurotul Aini Association of Higher Education Informatics and Computers https://orcid.org/0000-0002-7546-5721

DOI:

https://doi.org/10.34306/ijcitsm.v4i2.159

Keywords:

Predictive Models, Machine Learning (ML), System Development, Accuracy, Scalability

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.

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