<![CDATA[Purpose: Muhammadiyah Purin Elementary School has demonstrated a strong commitment to implementing deep learning as part of its educational transformation agenda. This study aims to analyze the implementation of the deep learning policy at Muhammadiyah Purin Elementary School using the policy implementation framework of communication, resources, disposition, and bureaucratic structure. Methods: This study employed a qualitative approach. Data were collected through interviews, observations, and document analysis, and analyzed using an interactive model consisting of data reduction, data display, and conclusion drawing. Data validity was ensured through source and technique triangulation. Findings: The findings indicate that the implementation of the deep learning policy has generally been effective. Communication regarding the policy has been disseminated through formal and informal channels, although teachers still require more technical guidance for classroom implementation. Adequate infrastructure and professional development programs are available, yet their utilization remains constrained by variations in teachers’ pedagogical and digital competencies. School leaders and teachers demonstrate strong commitment and positive attitudes toward learning transformation, despite challenges related to administrative workloads and differing levels of motivation. Furthermore, a clear organizational structure, collaborative coordination, and reflective academic supervision have supported policy implementation. However, continuous mentoring, technology optimization, and the strengthening of a collaborative learning culture remain necessary to sustain improvement. Research Implications: The study highlights that effective deep learning implementation can contribute to school quality improvement through strengthened teacher capacity, supportive leadership, optimized learning resources, and active parental involvement. Originality: This study contributes to the literature by examining the implementation of Deep Learning policies at the elementary school level. It provides empirical evidence on how communication, resources, dispositions, and bureaucratic structures influence Deep Learning implementation in the elementary school context, an area that remains underexplored in existing studies.]]>
