<![CDATA[The rapid growth of data volume, velocity, and variety has created significant challenges for traditional information systems, which are often unable to process large-scale data efficiently. This study aims to design and implement an efficient information system for big data processing using a distributed computing approach. The research adopts a systematic and experimental method consisting of system design, implementation, and performance evaluation. The proposed system is developed using a distributed architecture with parallel processing mechanisms to improve scalability and resource utilization. Performance evaluation is conducted using key metrics, including processing time, throughput, and efficiency improvement percentage, based on experimental testing with datasets ranging from 1 GB to 10 GB. The results show that the proposed system consistently reduces processing time and increases throughput compared to the baseline system. The system achieves efficiency improvements ranging from 33.3% to 36.9%, exceeding the predefined success indicator of 30%. These findings demonstrate that the integration of distributed computing and optimized system architecture significantly enhances big data processing performance. Therefore, the proposed system provides a scalable and practical solution for handling large-scale data processing in modern information systems.]]>
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