<![CDATA[Home is a basic need for humans in living life. Humans need a house to live and mingle with family. Having a decent home is the dream of every family. However, due to economic limitations, livable houses are difficult to realize. The government made the Rutilahu (Uninhabitable House) policy to reduce the number of uninhabitable houses. However, in practice there are still many misdirected targets. The Village Government is still carrying out the data classification process manually to determine which houses are livable and which are not. Processes that are still manual are old and inaccurate. For this reason, it is necessary to have a system to classify suitable and ineligible houses using the Support Vector Machine algorithm to make it more detailed so that later the assistance will not be misdirected. Support Vector Machine is a technique for maximizing margins, namely the distance that separates data classes by finding the best hyperplane. Determination of the classification of livable houses is based on four main indicators, namely the structure of the building, its area, sanitation, and clean water. This study took 642 data with 513 training data and 129 testing data and by using validation techniques using the confusion matrix obtained an accuracy of 80%. Thus the system built with the Support Vector Machine algorithm is quite good in the classification of livable houses]]>
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