Statistical downscaling (SD) analyzes relationship between local-scale response and global-scale predictors. The SD model can be used to forecast rainfall (local-scale) using global-scale precipitation from global circulation model output (GCM). The objectives of this research were to determine the time lag of GCM data and build SD model using PCR method with time lag of the GCM precipitation data. The observations of rainfall data in Indramayu were taken from 1979 to 2007 showing similar patterns with GCM data on 1 st grid to 64 th grid after time shift (time lag). The time lag was determined using the cross-correlation function. However, GCM data of 64 grids showed multicollinearity problem. This problem was solved by principal component regression (PCR), but the PCR model resulted heterogeneous errors. PCR model was modified to overcome the errors with adding dummy variables to the model. Dummy variables were determined based on partial least squares regression (PLSR). The PCR model with dummy variables improved the rainfall prediction. The SD model with lag-GCM predictors was also better than SD model without lag-GCM.

Statistical downscaling (SD) is a statistical technique used to predict local scale rainfall based on global atmospheric circulation. The global scale climate variable used is precipitation from GCM (Global Circulation Model). However, the precipitation data of GCM outputs have a large dimension, giving rise to multicollinearity in the data. This problem is handled by the Principal Component Regression (PCR) method. In addition, the SD models have heterogeneous error variances. The dummy variable is added to the PCR models to solve the problem. Hierarchical (k-means) and non-hierarchical cluster techniques (average linkage, median linkage, and ward linkage) are used in modeling to determine rainfall data groups. Furthermore, the group formed is the basis of the formation of dummy variables. This study aims to estimate local rainfall data in Pangkep district as a salt-producing area in South Sulawesi. There are 4 dummy variables based on the 5 groups formed. Dummy variables are able to improve predictions from the PCR models. R2 values of the PCR-dummy models (ranging from 89.89% to 95.58%) are relatively higher than the PCR models (ranging from 55.87% to 57.61%). This result is also consistent with the model validation stage. The PCR-dummy models based on non-hierarchical cluster techniques (k-means) are better than the PCR-dummy models based on cluster hierarchy techniques. In general, the best model is the PCR-dummy model of the non-hierarchical cluster technique (k-means ) and involves 4 main components.

The involvement of teachers in Bone Regency in using information and communication technology (ICT) to prepare teaching materials is very little or even never said, even though computer facilities and infrastructure are available in the computing lab. This activity aims to provide knowledge to Mathematics teachers about online learning Google Classroom and Geogebra. The use of Google Classroom will make learning more effective for teachers and students because learning is no longer limited by space and time, student can explore learning resources easily and utilize information technology properly. Likewise, Geogebra training is expected to overcome the difficulties of teachers in visualizing concept charts in mathematics dynamically. The target audience for community service is mathematics teachers who are members of the Mathematics MGMP in Bone Regency.

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Quantile regression can be used to analyze data containing outliers including DHF data. The spline is able to identify several patterns of change in the regression model, so this study uses a second-order quantile spline regression model in analyzing DHF data that occurred in Makassar City. In this article, the authors analyze the pattern of changes that occur in platelets based on changes in the hematocrit content of DHF patients. The selected quantiles are quartiles 0.25; 0.50; and 0.75 with 3-knot points. Based on the results of the analysis, the minimum GCV value obtained at the use of knot points is 30.30; 44.80; 47.10 for the 0.25 quartile; 0.50; and 0.75. This shows that in each quartile, there are four patterns of quadratic changes that occur in the platelet count of DHF patients. The parabolic curve formed in each pattern segmentation shows that there are times when platelets are increasing and there are times when platelets are decreasing. However, the average platelets decreased drastically, especially when the hematocrit reached 47.10%.