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his study examines the compliance of multinational companies with the Regional Minimum Wage (UMR) regulations in Indonesia and the factors contributing to violations. Although UMR is designed to protect workers' welfare, many companies still pay below the established standards. A qualitative research method was employed to collect data through in-depth interviews, field observations, and document analysis. The findings reveal that UMR violations occur across various industrial sectors, with violation rates ranging from 10% to 20%. Factors contributing to these violations include a lack of oversight, ineffective law enforcement, and legal uncertainties related to regulations that vary between regions. The research also identifies that UMR violations significantly impact workers' welfare, including difficulties in meeting basic needs and increased stress due to financial uncertainty. To enhance compliance, the study recommends improving oversight and law enforcement, harmonizing UMR standards across regions, and developing an anonymous complaint system for workers. These findings are expected to provide insights for policymakers, company management, and workers to improve the implementation of UMR regulations and enhance workers' welfare in Indonesia. [ABSTRACT FROM AUTHOR]

This study aimed to determine the influence of Digital Marketing on Competitive Advantage and MSME Performance. The research employed a Quantitative Method utilizing SPSS and SEM (Structural Equation Model) assisted analysis tools with the Lisrel program. The analysis results reveal that digital marketing exerts a positive and significant effect on competitive advantage, digital marketing significantly impacts MSME performance positively, competitive advantage significantly influences MSME performance positively, and Digital Marketing positively affects MSME performance through competitive advantage. [ABSTRACT FROM AUTHOR]

This article explores the use of the K-Means clustering algorithm to analyze sales data at XYZ Grocery stores. The research aims to improve inventory management strategies by understanding sales patterns. The study implements the K-Means algorithm to create groups of products based on their sales performance. While specific results and contributions are not provided, the findings are expected to offer insights into sales trends at the store. Further research could explore the practical applications of these findings in store management and inventory control. The article also discusses the design and testing of a system for managing product data, which proves to be reliable and efficient. The system effectively manages and stores product data, responds well to normal and incorrect data inputs, and provides valuable strategic information for decision-making. The article includes references to other studies on data clustering and related topics. [Extracted from the article]