Abstract: This research focuses on signal processing techniques in Management Information Systems to increase data accuracy and processing efficiency. The performance assessment for MIS integrated with signal processing in the study utilized simulation-based research, case studies, and quantitative analysis. To be precise, a prototype of the system was designed to be test-run with synthetic data to simulate real-life cases, leading to impressive noise reduction and enhancement of clarity in data. Additionally, these techniques are discussed through a case study-related approach, demonstrating various practical issues and advantages in enhancing organizations' capabilities for decision-making. Based on the DSR methodology, it was possible to develop and test a novel artifact including signal processing, in which an increase in the quality of data was identified, together with gains in the efficiency of its processing. Quantitative analysis via t-tests evidenced that the improvement was statistically significant by all key indicators, including MSE reductions and enhancements in processing time. These results show that embedding signal processing algorithms into MIS frameworks indeed ensures scalability and practicality for real-time applications along with enhancing system performance. The given research contributes to the growing field of MIS through its presentation of a robust framework for integrating techniques of advanced data processing effectively, thereby yielding better decision support systems and operational efficiency.

Keywords: Signal Processing, Noise Reduction, Scalability, Decision Support

Pages: 244-256

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