<![CDATA[In January 2020, it was confirmed that COVID-19 can be transmitted from human to human through the upper respiratory tract with a high infection rate. The number of COVID-19 cases worldwide continued to increase rapidly through close contact, droplets, and airborne transmission. In response, governments and the WHO implemented preventive measures, including COVID-19 treatment preparation, increased emergency healthcare capacity, and patient screening. Early detection of COVID-19 became crucial in taking action, providing treatment, and protecting others. In the Naïve Bayes algorithm, a potential issue arises with the possibility of zero probabilities for some features or attributes in the COVID-19 prediction training data. Therefore, Laplace Smoothing is used to address this problem. This study aims to compare the average accuracy rates of Gaussian Naïve Bayes and Categorical Naïve Bayes algorithms using different proportions of training data but the same testing data for COVID-19 detection. The methods used in this research are Gaussian Naïve Bayes and Categorical Naïve Bayes with Laplace Smoothing implemented using the Python library called scikit-learn. The research results show that the Gaussian Naïve Bayes algorithm without Laplace Smoothing has an average accuracy of 0.902165, while with Laplace Smoothing, it has an average accuracy of 0.973448. For the Categorical Naïve Bayes algorithm, without Laplace Smoothing, it has an average accuracy of 0.983864, while with Laplace Smoothing, it has an average accuracy of 0.984273. In conclusion, Laplace Smoothing plays a significant role in improving the average accuracy of Naïve Bayes algorithms. Categorical Naïve Bayes achieves the highest average accuracy of 0.9840685 (with and without Laplace Smoothing), while Gaussian Naïve Bayes achieves 0.947549 (with and without Laplace Smoothing). Categorical Naïve Bayes has a higher average accuracy compared to Gaussian Naïve Bayes.]]>
