The Application of Logistic Regression to Public Service Satisfaction in Tangga Batu Village

Abstract

From a government administration perspective, public services at the village level are often assessed in a conventional and subjective manner. The purpose of this study is to evaluate the effectiveness of using the Binary Logistic Regression method to model and classify the level of community satisfaction with public services in Tangga Batu Village, Seluma Regency. This study employs a quantitative approach, using primary data collected from 276 respondents via a structured SERVQUAL questionnaire based on five dimensions: Tangible Evidence (X1), Reliability (X2), Responsiveness (X3), Assurance (X4), and Empathy (X5), all of which were validated using Pearson’s correlation test. Modeling was performed using the Python programming language, with 80% of the data allocated for training and 20% for testing. The results showed that the Binary Logistic Regression model was able to classify community satisfaction with an accuracy of 67.9% on the test data and an AUC-ROC of 0.755, as well as an average accuracy of 65.2% in the 5-fold cross-validation test. Based on the Wald test, the variables Empathy (X5) and Assurance (X4) were found to have a statistically significant effect on citizen satisfaction (p<0.05), with Odds Ratios of 1.8787 and 1.6610, respectively, while the other three variables were not significant. With an average predicted response time of 0.9795 milliseconds per sample, this model proved to be highly computationally efficient. This study concludes that the binary logistic regression method can be utilized by the Tangga Batu Village Government as an objective, data-driven decision-making tool to determine priorities for service improvements, particularly in enhancing the friendliness and personal attention of staff, as well as the reliability and transparency of administrative services.