<![CDATA[The level of life satisfaction of commuter workers in Indonesia is classified using the K-Nearest Neighbor (K-NN) algorithm using the RapidMiner application. This study aims to provide a better understanding of the social and economic conditions of workers who have to travel long distances every day. To collect data, a questionnaire covering various information such as income, number of dependents, location of residence, travel time, and level of life satisfaction was sent. Before being entered into the model, the data is then processed through a cleaning stage, normalizing numeric values, and dividing into test data and training data. One of the reasons for RapidMiner is its visual interface, which allows users to create classification models without writing programming code. The test results show that the K-NN algorithm can accurately classify the level of life satisfaction of commuter workers. Model performance is greatly influenced by the selected variables, namely the K value, and data quality. This study is expected to help related parties, this approach is considered effective in helping data-based decision making. ]]>
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