Analysis Of The Effectiveness Of Decision Support Systems And Operational Processes On Improving Service Performance At Perum Bulog Baubau Branch

Abstract

This research was motivated by the development of information technology which requires organizations, especially public institutions, to improve the quality of decision-making and operational effectiveness in supporting service performance. At Perum Bulog Baubau Branch, several problems were still found, such as rice stock accumulation, delays in distribution, and weak supervision, indicating that the decision support system and operational processes had not been running optimally. This study aims to determine the influence of the Decision Support System (DSS) and operational processes on service performance at Perum Bulog Baubau Branch.

The research method used was a quantitative descriptive method with a survey approach. The population of this study consisted of all partners who became permanent customers of Perum Bulog Baubau Branch in 2025 in Baubau City. The research sample consisted of 50 respondents selected using probability sampling with a simple random sampling method. Data were collected through observation, interviews, and questionnaires. Data analysis was carried out using IBM SPSS Statistics through validity tests, reliability tests, classical assumption tests, multiple linear regression analysis, t-tests, F-tests, and coefficient of determination analysis.

The results showed that partially the Decision Support System (X1) had a  positive and significant effect on service performance (Y) with a t-value of 4.792 > 1.677 and a significance value of 0.000 < 0.05. Operational processes (X2) also had a positive and significant effect on service performance with a t-value of 2.849 > 1.677 and a significance value of 0.006 < 0.05. Simultaneously, the Decision Support System and operational processes had a positive and significant effect on service performance. However, the coefficient of determination test (Adjusted R Square) showed that the independent variables influenced the dependent variable by 51.2%, while the remaining 48.8% was influenced by other variables outside this study