This research discusses about the Ordinary Least Squares (OLS) method and robust M-estimation method; compare between the Tukey bisquare and Huber weighting from simple linier regression models that contain outliers. Data are generated through simulation with the percentages of outliers and sample sizes. Each data will be formed into a simple linier regression model, then the percentage of outliers, RSE and MAD values are calculated. The results show that RSE and MAD values produced by a simple linear regression model with the OLS method are influenced by the percentage of outliers. However, the regression model of robust M-estimation with sample size 30, 60, 90, 120, and 150 results an unstable RSE values with the change of the percentage of outlier and the MAD values that are not affected by the percentage of outliers and sample size. The robust M-estimation method with Tukey Bisquare weighting is as good as the Huber weighting. Full Article

According to the Student Status data of FMIPA UNSOED,  from 190 students of the Mathematics Department 2015-2017, there were only 22 students who graduated no more than 4 years (for 8 semesters). This study aims to determine the factors that influence the completion of the final project of the 2015-2017 UNSOED Mathematics Department students. This study uses primary data obtained by distributing questionnaires to students of the Department of Mathematics class 2015-2017 UNSOED and secondary data obtained from Bapendik FMIPA UNSOED. The method used in this research is factor analysis with 10 independent variables. The results showed that there were 3 factors that influenced the completion of the final project of the 2015-2017 UNSOED Mathematics Department students. The first factor is the student's persistence factor, the second is the supervisor's factor, and the third is the student's ability factor.

Increasingly strong competition in intercollegiate and students who increasingly selective and knowledgeable require universities to continue to improve his ministry. Matter requires a knowledge of the conditions for this service, that has adapted to student expectations or not . This study aimed to determine the extent to which level the match between the expected service quality and perceived student at Department of Mathematics and Natural Sciences Unsoed and explain the influence of the five dimensions of service quality that is tangibles, reliability, responsiviness, assurance and empathy for student satisfaction and loyalty. Results of the study was assessment of the quality of student services at the Department of Mathematics and Natural Sciences Unsoed is perceived service quality is still below the expected service quality , perceived still not satisfactory. Five dimensions of service quality consisting of tangibles, reliability, responsiveness, assurance, and empathy, positive effect on student satisfaction. Satisfaction indirect influence on student loyalty and there is a direct influence of the five dimensions of service quality on loyalty through student satisfaction. Student loyalty translated in expressing positive things about the Department of Mathematics and Natural Sciences to others , recommending courses at the Department of Mathematics and Natural Sciences to the others, recruiting new employees from alumni, and ready to provide assistance if needed.

This paper discusses aselection of smoothing parameters for the linier spline regression estimation on the data of electrical voltage differences in the wastewater. The selection methods are based on the mean square errorr (MSE) and generalized cross validation (GCV). The results show that in selection of smooting paranceus the mean square error (MSE) method gives smaller value , than that of the generalized cross validatio (GCV) method. It means that for our data case the errorr mean square (MSE) is the best selection method of smoothing parameter for the linear spline regression estimation.