<![CDATA[Suboptimal management of office supplies causes significant budget waste in the public sector. This study addresses this issue by integrating the K-Means algorithm and the Socialization, Externalization, Combination, dan Internalization (SECI) knowledge management model to optimize logistics budget efficiency at the Palembang DPRD Secretariat. K-Means was utilized to partition the 2025 supply expenditure data into three priority clusters based on budget absorption and demand frequency. To ensure analytical outputs influence managerial decisions, K-Means was positioned as the primary explicit-to-explicit transformation engine within the SECI combination phase. The integration successfully transformed raw transaction data into a data-driven Standard Operating Procedure (SOP). Quantitative analysis reveals that a small subset of items in Cluster C3 accounts for a disproportionately high share of total budget absorption. Consequently, supervision can now strictly target these high-budget anomalies such as the Rp52.2 million spent on specific folio paper significantly reducing potential leakage and improving allocation efficiency. The main scientific contribution of this study is a novel framework that bridges mathematical data extraction and managerial policy formulation. This integrated approach is proven to measurably enhance regional budget efficiency.]]>
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