<![CDATA[Buses are the most popular land transportation during the pandemic by the community, because using buses can save time compared to using sea transportation. Transportation using land routes in modern times is considered to be the most effective means. The bus ticket price itself is quite volatile, sometimes very expensive and sometimes very cheap because it depends on the price indicators that go up and down. The purpose of this research is to design a system that can predict the price of airline tickets early by using pattern recognition technology with Backpropagation by utilizing matlab software. In the prediction system there are only six input variables, so the researcher suggests that further research is expected to have more detailed input variables to be used as test data and target data because there may still be many indicators that cause bus prices to fluctuate. After knowing the indicators of rising and falling ticket prices, the next ticket must be done first. Initially, the indicators will be tested and tested to find out using the backropogation method. The results of flight ticket price predictions using an architectural network show that the proportion of similarity of each difference is able to improve the weighting of the hidden layer with the output of UP ticket prices. the indicators will be tested and tested to find out using the backropogation method. The results of flight ticket price predictions using an architectural network show that the proportion of similarity of each difference is able to improve the weighting of the hidden layer with the output of UP ticket prices. the indicators will be tested and tested to find out using the backropogation method. The results of flight ticket price predictions using an architectural network show that the proportion of similarity of each difference is able to improve the weighting of the hidden layer with the output of UP ticket price]]>
