Literature Study: Highway Traffic Management with Sentiment Analysis and Data Mining

Abstract

In today's era, technology is growing rapidly, many of the latest technologies are in great demand by the Indonesian people, one of which is social media. Various social media such as Facebook, Twitter, Instagram, have become very popular applications for various ages, including teenagers, adults, and the elderly. Social media has a positive impact that can help people convey the latest information through posts on their respective accounts. Social media can disseminate information in a short time, this is why social media is an interesting application to research. The problem of road traffic congestion is strongly influenced by the number of vehicles that pass every day. A large number of private vehicles and public vehicles that pass greatly confuses the atmosphere of highway traffic. Congestion often occurs during working hours. Road congestion also often occurs when an unwanted incident occurs. Sentiment analysis algorithms and data mining algorithms can be combined to find information on traffic jams through social media such as Facebook, Twitter, Instagram, and other social media. The results show that sentiment analysis methods and data mining algorithms can be used to find information about current traffic jams through social media. The conclusion from this literature study can be seen that the K-Nearest Neighbor data mining algorithm is the best choice to overcome road traffic congestion, which will then be further developed in the form of highway traffic management modeling.