Statistical Downscaling to Predict Monthly Rainfall Using Generalized Linear Model with Gamma Distribution

Statistical Downscaling (SDS) models might involve ill-conditioned covariates (large dimension and high correlation/multicollinear). This problem could be solved by a variable selection technique using L1 regularization/LASSO or a dimension reduction approach using principal component analysis (PCA). In this paper, both methods were applied to generalized linear modeling with gamma distribution and compared to predict rainfall models at 11 rain posts in Indramayu. More over, generalized linear model with gamma distribution was used to obtain non-negative rainfall prediction and compared with principal component regression (PCR). Two types of ill-conditioned data with different characteristics (CMIP5 and GPCP version 2.2) were used as covariates in SDS modeling. The results show that three methods (PCR, Gamma-PC, and Gamma-L1) did not demonstrate significant difference in term of Root Mean Square Error (RMSE) after addition of dummy variables (month) in the models. However, a generalized linear modeling with gamma distribution could be considered as the best methods since it provided non-negative rainfall predictions

FUNCTION GROUP SELECTION OF SEMBUNG LEAVES (BLUMEA BALSAMIFERA) SIGNIFICANT TO ANTIOXIDANTS USING OVERLAPPING GROUP LASSO

Functional groups of sembung leaf metabolites can be detected using FTIR spectrometry by looking at the spectrum's shape from specific peaks that indicate the type of functional group of a compound. There were 35 observations and 1866 explanatory variables (wavelength) in this study. The number of explanatory variables more than the number of observations is high-dimensional data. One method that can be used to analyze high-dimensional data is penalized regression. The overlapping group lasso method is a development of the group-based penalized regression method that can solve the problem of selecting variable groups and members of overlapping groups of variables. The results of selecting the variable groups using the overlapping group lasso method found that the functional groups that were significant for the antioxidants of sembung leaves were C=C Unstructured, CN amide, Polyphenol, Sio2

Pengendalian Koefisien Regresi Least Absolute Deviation pada Rentang Bermakna Menggunakan Program Linier

So far, regression analysis is used to model the mean of response variable as a function of some independent variables, using the least squares (LS) method. In general, the LS method is able to describe well the measure of central tendency, however it is not robust against outliers. Therefore, in certain cases, a regression analysis that minimizes the sum of absolute residuals (least absolute deviation - LAD) is required, which is more robust against outliers. So far, the value of the regression coefficient is not modeled and only depends entirely on the data processed. In some cases, the sign and the value of regression coefficients need to be controlled, in order to be in the meaningful range. The results of this study showed that the modification of the constraints on the LAD regression able to control the regression coefficients to be in the meaningful range. The results of bootstrap showed that distribution of controlled regression coefficients were different from distribution of uncontrolled regression coefficients

Analisis epistimologi Burhani dalam pembelajaran PAI

Pendidikan agama islam semakin hari turut menjadi perhatian, bukan karena hal baik melainkan sebaliknya, pembejaran PAI cenderung monoton karena banyak disajikan dengan cara ceramah, ditambah lagi disajikan tanpa mencoba menghadirkan akal pikiran siswa secara logis atau bersifat doktirnal, tentunya dalam hal ini siswa akan banyak meraba-raba dalam pikirannya apa yang disampaikan oleh guru. Tujuan penerapan metode burhani dalam pembelajaran PAI adalah menanamkan nilai-nilai islam melalui pendekatan akal pikiran sehingga bisa diimplementasikan dalam menjalani hidup sehari-hari. Metode ini menerapkan penelitian kualitatif deskriptif yang bersifat Library Researc dengan pendekatan conten analisi. Penelitian ini menghasilkan sebuah kesimpulan bahwa meletakkan dasar pengetahuan islam akan mudah dilakukan apabila disampaikan berdasarkan kontekstual, artinya akal pikiran siswa dapat menerima apabila apa yang diajarkan dekat dengan pengalamannya, bukan sekedar penyampaian yang bersifat doctrinal yang menimbulkan pembelajaran menjadi monoton. Untuk mencapai tujuan tersebut maka ada dua metode yang bisa diimplementasikan yaitu metode amsal dan saintifik burhani. Untuk itu implementasi burhani lebih mendorong siswa untuk lebih kritis terhadap berbagai persoalan sehingga pembelajaran bersifat konstruktivistik, keaktifan siswa menjadi nilai lebih dalam peroses pembelajaran karena terdorong untuk mengkomunikasikan realitas yang ditangkap secara masuk di akal sesuai dengan konsep rasionalitas logika

Pemodelan Spasial Rasio Utang Pemerintah di Negara G20 Tahun 2003-2017

The ability to manage government debt is very important for a country. The government debt to GDP ratio, indicating a country ability to pay its debts, is often used as a limit to the amount that a government can issue. By using Geographically Weighted Panel Regression (GWPR) with location distance weighting, this study is aimed at describing the distribution pattern, classifying, and modelling the government debt ratios in G20 countries by observing spatial effects. The results show that the GWPR is the best model compared to the global panel regression in identifying that the government debt is influenced by inflation, final consumption, and population growth

Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms

GDP is very important to be monitored in real time because of its usefulness for policy making. We built and compared the ML models to forecast real-time Indonesia's GDP growth. We used 18 variables that consist a number of quarterly macroeconomic and financial market statistics. We have evaluated the performance of six popular ML algorithms, such as Random Forest, LASSO, Ridge, Elastic Net, Neural Networks, and Support Vector Machines, in doing real-time forecast on GDP growth from 2013:Q3 to 2019:Q4 period. We used the RMSE, MAD, and Pearson correlation coefficient as measurements of forecast accuracy. The results showed that the performance of all these models outperformed AR (1) benchmark. The individual model that showed the best performance is random forest. To gain more accurate forecast result, we run forecast combination using equal weighting and lasso regression. The best model was obtained from forecast combination using lasso regression with selected ML models, which are Random Forest, Ridge, Support Vector Machine, and Neural Network

Analisis Perubahan Penggunaan Lahan Kecamatan Jaten Kabupaten Karanganyar Tahun 2014 Dan 2021

The study conducted on changes in land use was in the District of Jaten, Karanganyar Regency. Changes in land use in the District of Jaten are very interesting to study, because the entire District of Jaten is a self-sufficient village of rice, besides that the District of Jaten is an industrial center in Karanganyar Regency. The background that makes Jaten Sub-district an industrial center is because it has a strategic area because it is located on the main route of Surakarta-Sragen and Karanganyar-Sragen. This study aims (1) to analyze the distribution of land use change in Jaten Subdistrict, Karanganyar Regency in 2014 and 2021, (2) to analyze the distribution pattern of land use change in Jaten Subdistrict, Karanganyar Regency in 2014 and 2021, (3) to analyze the main factors that influence the pattern of change. land use in the District of Jaten. In this study using the interpretation of satellite imagery and field surveys. Sampling using purposive sampling method in conducting data validation in the field. Overlay with GIS technology is used to determine the distribution of land use changes that occur in Jaten District in 2014 and 2021. To find out the distribution pattern of land change using Nearest Neighbor Analysis. Factors that affect the pattern of land change distribution can be identified by the interview method which is then processed with a frequency table. Changes in land use in the District of Jaten for a period of 8 years, from 2014 to 2021, there was a change in land use covering an area of 122.36 Ha. The area that experienced the most changes was Sroyo Village with a change of 28.87 Ha with a percentage of 24%. Meanwhile, the village that experienced the least change was Suruh Kalang Village, with a change in land use of 1.24 Ha with a percentage of 1%. The distribution pattern of land use changes in the Central Java District is classified in a clumped classification. With an NNR value of 0.539757 and a Z-score of -15.376. The main factor that influences the pattern of land change distribution in Jaten District from the interview results is population density with a percentage of 18.9%

Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms

GDP is very important to be monitored in real time because of its usefulness for policy making. We built and compared the ML models to forecast real-time Indonesia's GDP growth. We used 18 variables that consist a number of quarterly macroeconomic and financial market statistics. We have evaluated the performance of six popular ML algorithms, such as Random Forest, LASSO, Ridge, Elastic Net, Neural Networks, and Support Vector Machines, in doing real-time forecast on GDP growth from 2013:Q3 to 2019:Q4 period. We used the RMSE, MAD, and Pearson correlation coefficient as measurements of forecast accuracy. The results showed that the performance of all these models outperformed AR (1) benchmark. The individual model that showed the best performance is random forest. To gain more accurate forecast result, we run forecast combination using equal weighting and lasso regression. The best model was obtained from forecast combination using lasso regression with selected ML models, which are Random Forest, Ridge, Support Vector Machine, and Neural Network

Pemodelan Tingkat Suku Bunga Surat Perbendaharaan Negara 3 Bulan

One of the basic macroeconomic assumptions that is still experiencing difficulties developing an accurate economic model is the 3-month Treasury bill (Surat Perbendaharaan Negara/SPN). This is mainly caused by its irregular data period, based on the average yield won in an auction held at a certain period. This study aims to build 3-month Treasury Bill (SPN) interest rate models by comparing several time-series methods, namely spline smoothing, exponential smoothing, moving average smoothing, and a regression model using s spread with one year Government Bond yield (Surat Utang Negara/SUN). This study shows that the spline smoothing method and regression analysis with one year SUN is the best model. In contrast, spline smoothing is better for short-term projections, and regression with one year SUN is better for medium-term projection