Author
Rakhmadi Rahman(1
), Muh Revan(2), Nur Safikah(3),
(1) Sistem Informasi Institut Teknologi Bacharuddin Jusuf Habibie Parepare, Indonesia,
(2) Sistem Informasi Institut Teknologi Bacharuddin Jusuf Habibie Parepare, Indonesia,
(3) Sistem Informasi Institut Teknologi Bacharuddin Jusuf Habibie Parepare, Indonesia,
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Available online: 2024-07-29 | Published : 2024-07-29
Copyright (c) 2024 Rakhmadi Rahman, Muh Revan, Nur Safikah
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Abstract
Windows operating system, which is one of the largest and most popular computing platforms in the world, faces the challenge of continuously improving performance in the face of increasing user demands and rapid technological developments. Machine learning, as a branch of artificial intelligence, offers an innovative solution to overcome this challenge by utilizing algorithms to learn from data and system usage patterns. In this study we use the Experimental Research Method, This method can be used to test the effectiveness of implementing machine learning techniques in improving the performance of the Windows operating system. Experimental research can be done by creating experiments in a controlled environment, for example, using simulations or testing on a specially prepared development environment. This article discusses how the application of machine learning can improve Windows performance through optimization of memory management, task scheduling, and power management. By conducting controlled experiments to evaluate machine learning techniques, this study shows that this technology can significantly improve operational efficiency, security, and user experience, making Windows a more responsive and efficient platform.
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References
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