<![CDATA[The use of mobile applications is increasingly important in daily life in the digital era. However, the abundance of application choices in the Play Store makes it difficult for users to choose the right application according to their needs. Not only that, application developers also have difficulty finding the most liked ratings by users and the type of application that is widely downloaded. This research aims to find a solution to the problems of users and developers by comparing the performance of Naive Bayes and Logistic Regression algorithms in classifying Google Play Store application data based on ratings and the type of application that is most downloaded by users. The results show that both algorithms have a high level of accuracy, but Naive Bayes has a higher level of accuracy than Logistic Regression. Naive Bayes obtains an accuracy rate of 92.63% while Logistic Regression obtains an accuracy rate of 92.60%. This research provides guidance for users to choose the right algorithm in classifying Google Play Store application data. However, these results are based on the data used in a particular study, so they cannot be generalized to all situations and datasets. Other factors such as data quality and proper feature selection can also affect algorithm performance. In addition, this research also shows that the type of application that is most downloaded by users on the Google Play Store is a free application. This can be input for developers to develop types of applications that are favored by users. This research also shows the results of application ratings, where an application named Life Made WI-FI Touchscreen Photo Frame with the photo editing category gets a very high rating. These results can be input or references for application developers to create even better applications in the future..]]>
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