Interacting with others is an essential part of human life.. Sometimes users experience problems using chat applications, namely language differences when communicating with foreigners. Based on these problems, the author aims to build a chat application with a mobile-based automatic translator. In this application, the user can choose the language that will be used as needed. This application development uses the React Native framework for mobile applications and uses the NLP translation API for translators. This chat application automatically translates messages into the language used by the user. After doing some testing on the application, it can be concluded that it is according to the design and can make it easier for users to communicate with different languages

Technological advances have undoubtedly had a major impact on information media. One impact of technological progress is the existence of news media as a source of publik information. There is also regional information, both domestic and foreign, and of course there are various discussions. News data from online news portals can be used as a source of information as a source of research and analysis. Of course, newa portals cover all types of news on various topics. Indentifying frequently discussed topics on news portal will denfinitely take a lot of time. Therefore, this research focuses on applying a topic modelling system to implement a news topic decision system using the Latent Dirichlet Allocation (LDA) method. This research successfully applies the Latent Dirichlet Allocation (LDA) method in determining news topics, of which there are three topic categories that are often discussed on the online news portal detik.com. topic 1 contains natural disaster event, topik 2 contins political figures and issues, topik 3 conttains news about the world cup.