<![CDATA[In this paper, we try to explain how to formulate the derivation of the Naive Bayes concept and apply it to a simple case. This is because usually users only use existing formulas or tools that are already available in a programming language regardless of where the formulas are implemented in the available tools come from. To familiarize users with understanding the state of art rather than a formulation, in this study we try to combine the concept and application of the Naive Bayes model formulation. Starting with the elaboration of the concept of derivation of the Naive Bayes formula, then we take a case study to begin to provide an overview of the implementation of the formula. In this study, we apply Naive Bayes to predict learning outcomes before ending at the end of the semester. The dataset was constructed using daily scores from student activity and quizzes. The calculation of this algorithm is enough to use the R language with case sampling in 4 classes of language theory and automata even semester 2017-2018 at the Department of Informatics Engineering, Faculty of Engineering, University of Muhammadiyah Ponorogo with a dataset size of 99 records (99 students) which are divided into 70 records for training data and the rest for test data. The final result is that the prediction accuracy is 78.6%, with the conclusion that the use of the Naive Bayes concept is good enough to be used to predict in helping]]>
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