Zero: Jurnal Sains, Matematika, dan Terapan E-ISSN: 2580-5754 P-ISSN: 2580-569X Vol. 4, No. 1, Juni 2020, pp. 21-27  21 Journal homepage: http://jurnal.uinsu.ac.id/index.php/zero/index Analysis of Efficiency of Least Trimmed Square and Least Median Square Methods in The Estimation of Robust Regression Parameters Hamdan Abdi 1 , Sajaratud Dur 2 , Rina Widyasar 2 , Ismail Husein 2 1 Department of Mathematics, Universitas Andalas, Padang, Indonesia 2 Department of Mathematics, Universitas Islam Negeri Sumatera Utara Medan Article Info ABSTRACT Article history: Received January 02,2020 Revised Februari 03, 2020 Accepted March 04, 2020 Robust regression is a regression method used when the remainder's distribution is not reasonable, or there is an outreach to observational data that affects the model. One method for estimating regression parameters is the Least Squares Method (MKT). The method is easily affected by the presence of outliers. Therefore we need an alternative method that is robust to the presence of outliers, namely robust regression. Methods for estimating robust regression parameters include Least Trimmed Square (LTS) and Least Median Square (LMS). These methods are estimators with high breakdown points for outlier observational data and have more efficient algorithms than other estimation methods. This study aims to compare the regression models formed from the LTS and LMS methods, determine the efficiency of the model formed, and determine the factors that influence the production of community oil palm in Langkat District in 2018. The results showed that in testing, the estimated model of the regression parameters showed the same results. Compared to the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity influence the production of palm oil smallholders in Langkat District in 2018. as well as the comparison of the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity are factors that influence the production of palm oil smallholders in Langkat District in 2018. as well as the comparison of the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity are factors that influence the production of palm oil smallholders in Langkat District in 2018. Keywords: Robust Regression, Outliers, Least Trimmed Squares, Least Median Squares. This is an open access article under the CC BY-SA license. Corresponding Author: Hamdan Abdi, Department of Mathematics, Universitas Andalas, Padang, Indonesia Email: hamdanabdi10@gmail.com 1. INTRODUCTION Regression analysis is a statistical method used to determine the form of the relationship between two or more variables. The method used to estimate the parameters of the regression model is the Least Squares Method (MKT). Rousseeuw and Leroy (1987) explain that MKT is used because it is easy to calculate, but is easily influenced by outliers. Making an analysis by excluding outliers from data is not a proper procedure, because sometimes outliers provide information that cannot be provided by other data points. Therefore we need an alternative method that is robust to outliers, namely robust regression.