<![CDATA[This research is based on late payment data from UD Delta customers which affect all production processes that occur in UD Delta. The purpose of this study is to predict the character of the customer as a basis for analyzing the delay in the payment process that hinders the company's production. The datasets tested were 30% of the 600 data, which were divided into 1 id, 7 attributes and for the prediction classes 'No Problem', 'Slightly Problematic', and 'Very Problematic'. This study collects data by means of non-participant observation, namely data on UD Delta's bookkeeping. The process stage is processed using the python programming language with classification methods and naive bayes algorithms. The results obtained from this study are in the form of calculations that state the number of customers grouped into 3 categories, namely with correct predictions as much as 51%, incorrect predictions as much as 49%. From the results obtained, it will be taken into consideration by the company in processing the existing processes within the company as a step to develop the business carried out so that it is more optimal.]]>
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