This study would work on topic modeling focused on the algorithm employing Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The data collection of news announcements, that were published between 2020 and 202, is used as the main data resours with unstructed text. The stages of preprocessing include cleansing, stemming, and stop words. The advantages of LSA are fast and easy to implement. LSA, on the other hand, doesn’t consider the relationship between documents in the corpus, while LDA does. This study shows that LDA gives a better result than LSA.