Perbandingan XGB Regressor dengan Algoritma Lain untuk Prediksi Tarif Tol - Al Khairi, Said; Adriansyah, Ahmad Rio; Rosyidi, Lukman (DBESTI: Journal of Digital Business and Technology Innovation, 2025)
In recent years, toll roads in Indonesia have grown rapidly, many of which were built to facilitate traffic in developed areas and improve the distribution of goods and services to support economic growth. In addition, toll roads play an important role as part of efforts to improve connectivity between cities and regions and accelerate community mobility. Many benefits of toll roads have been felt by the people of Indonesia such as, the Jagorawi toll road which smooths traffic so as to shorten the travel time from one region to another, and many more. The purpose of this research is to create a machine learning prediction of toll road tariffs to provide a reference to the public, optimise toll tariffs in Indonesia, and provide input on toll tariffs as a consideration for the relevant government. This research approach is quantitative using linear regression with XGB Regressor algorithm. The results of making machine learning toll tariff predictions are quite accurate where the accuracy test results using the root mean squared error (RMSE) metric are at 3390.691, with the testing results showing that there are several predicted tariffs that match the original tariff.