<![CDATA[Data classification is a crucial process in data analysis that aims to categorize data into predefined categories. The Naive Bayes method has proven to be effective in data classification by leveraging the Bayes theorem. In this research, we discuss the implementation of Naive Bayes using the Python programming language for data classification. Firstly, we collect and prepare the dataset to be used in the classification process. Next, we implement the Naive Bayes algorithm using Python, which involves calculating class probabilities and feature probabilities based on the training data. We utilize available Python libraries to facilitate the implementation of this algorithm. After training the classification model using the training data, we conduct testing using separate testing data. We analyze the model's performance using evaluation metrics such as accuracy, precision, recall, and F1-score to measure the accuracy and performance of the Naive Bayes classification model.The results of the research show that the implementation of Naive Bayes using Python provides good performance in data classification. The resulting model is capable of classifying data with adequate accuracy and relatively fast execution time. The advantages of this implementation include the ease of use and flexibility of Python as a programming language. This study provides better insights into the implementation of Naive Bayes using Python in data classification. This method can be applied in various applications such as text analysis, spam detection, or document classification. We recommend further research to expand the use of Naive Bayes on larger datasets and compare it with other classification methods to gain a more comprehensive understanding of the performance and advantages of this algorithm.]]>
