Decision Support System For Community Welfare Assessment Using Fuzzy Logic Mamdani In Ponorogo Regency

The inequality of public welfare is still an important issue in various regions, including Ponorogo Regency. Manual determination of welfare levels often leads to inaccuracies due to subjectivity and limited data coverage. This study develops a Decision Support System (DSS) based on the Mamdani fuzzy logic method to objectively classify the level of community welfare in Ponorogo Regency. The system was built using Python and Streamlit, utilizing secondary data from the Central Statistics Agency (BPS) covering 14 indicators in the education, health, and demographic sectors across 21 sub-districts. The classification results group the sub-districts into three categories: low, medium, and high welfare. Of the 21 sub-districts, seven are classified as high, thirteen as medium, and one as low. The system achieves an accuracy rate of 80.95% when compared to ground truth data, indicating its reliability in reflecting real conditions. To complement this analysis, the Analytical Hierarchy Process (AHP) was applied to determine the relative importance of the indicators, resulting in education (0.54) as the most influential criterion, followed by health (0.30) and demographics (0.16). These findings show that the fuzzy Mamdani method is more suitable for data-driven classification, while AHP provides complementary insights into indicator prioritization. Therefore, this system offers not only a technical tool but also a strategic resource for evidence-based policy formulation by local governments.