Big data prototype development in Hadoop ecosystem using HDFS and mapreduce as the parallel computation model - Case study: Samples of intelligent transportation of Surabaya City

Social life is developing rapidly, as well as the transportation industry is also facing unprecedented challenges. Surabaya City is one of the regions that experienced this impact, which is the challenge of the rapid growth of private vehicles. Therefore, an adequate public transportation system is needed by providing a route that fits the society of Surabaya City. Observations of vehicle travel paths that pass in the city of Surabaya can be used as a preference for the route traversed by the community. These preferences can be used as a reference for the Surabaya City Government, especially the Transportation Service, as the basis for opening public transportation routes in Surabaya. By utilizing big data, the vehicle path data can be processed so that the results of the vehicle paths that are most frequently travelled by the society of Surabaya City. Big data is applied to the Hadoop ecosystem using HDFS and MapReduce as the parallel computation model. A similar computation model is used because the processed information is extensive, which will be more efficient than the sequential computing model. Due to the vast area of Surabaya City and the infrastructure to acquire the data needed, the researchers took data on a sample of 5 CCTVs points; those are Kayoon street, Panglima Sudirman, Urip Sumoharjo street, Keputran, and Raya Ngagel street. Based on the results of data processed at the 5 CCTVs points, it was found that the most frequently travelled route was the travel route from Panglima Sudirman street headed for Urip Sumoharjo street.