<![CDATA[This research is a development of Random Sample Consensus (RANSAC) technology which is one of the facilities in autonomous type electric vehicles. Random Sample Consensus (RANSAC) is a two-dimensional technology that detects the presence of objects so that the vehicle can respond in the form of braking or maneuvering to avoid these objects. In the application of RANSAC, it is often constrained by anomaly data which affects the accuracy in the detection of actual objects. In this study, it aims to overcome anomaly data. In this study, it was assisted by MATLAB software which was used for analysis, comparison and programming whose results were entered into Excel as a dataset for the Reconstruction of 2-Dimensional Objects Using RANSAC Modifications The result data will be processed using the Linear Regression method or prediction based on previous data and using the Least Square method or the Least Squares method. The results of the study used samplesize and maxdistance which varied, from the first test getting 92.8% of the data declared good and the second test getting 58.33% of the data declared good. The results of this study show that the reconstruction of 2-Dimensional objects from LiDAR data can be validated using the RANSAC method, the Robust Fit line and the Least Square Fit from the image can be changed by setting SampleSize and MaxDistance, in the test results that have been carried out the tested data are declared successful, and the amount of data and the determination of the amount of data can affect the results in the study.]]>
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