<![CDATA[Residents' well-being is a crucial factor determining the quality of life in a community. Planning targeted socioeconomic development programs is essential to achieving equitable distribution of resident welfare. However, diverse socioeconomic conditions and the manual process of mapping welfare data present challenges for decision-making at the neighborhood association/Rukun Tetangga (RT) level. This research aims to design a web application based on the random forest algorithm capable of predicting the level of welfare of residents in RT 002 RW 012. The research method involved collecting data from various welfare indicators, which were analyzed using the random forest algorithm to generate a classification of residents' welfare levels. The results showed that the random forest algorithm was able to predict welfare levels with high accuracy, with a confusion matrix test result of 90.33%. Other tests, including cross-validation, achieved 95.83% accuracy., This research also resulted in a system to simplify the management of resident data and socioeconomic conditions. This system can assist neighborhood association/Rukun Tetangga (RT) administrators in making more effective and data-driven decision-making.]]>
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