<![CDATA[The development of information technology (IT) globally has driven digital transformation in various sectors, including education. The rapid development of information technology (IT) in the education sector necessitates efficient and cost-effective infrastructure management. Budget limitations remain a major challenge for educational institutions in providing adequate hardware and software resources. Open Source Software (OSS) has emerged as a viable alternative due to its potential to reduce licensing costs while maintaining system functionality. This study aims to analyze the contribution of Open Source Software to improving the cost efficiency of information technology infrastructure at SMAN 1 Cicurug using a machine learning approach. This research employs IT inventory data consisting of Windows-based computers, Chromebooks, and smartphone devices utilized by the school community. Infrastructure cost data collected before and after OSS implementation were analyzed using machine learning algorithms, particularly regression and clustering techniques, to identify cost-saving patterns and efficiency levels. The application of machine learning enables an objective, data-driven evaluation of the financial impact of OSS adoption. The findings demonstrate that the implementation of Open Source Software significantly reduces software licensing costs and overall IT infrastructure maintenance expenses. Additionally, the use of machine learning models proves effective in supporting strategic decision-making related to IT infrastructure management. These results indicate that OSS adoption can serve as a sustainable and scalable solution for educational institutions seeking to enhance cost efficiency in information technology infrastructure.]]>
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