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Articles 271 Documents

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ANALISIS DATA INFLASI DI INDONESIA PASCA KENAIKAN TDL DAN BBM TAHUN 2013 MENGGUNAKAN MODEL REGRESI KERNEL Suparti, Suparti
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This study aims to analyze inflation in Indonesia after the goverment raised the price of electricity basic and fuel price in 2013 using kernel regression models. This method was good for data modeling inflation in Indonesia before. The goodness of a kernel regression model is determined by the chosen kernel function and wide bandwidth used. However, the most dominant is the selection of the wide bandwidth. In this study, determination of the optimal bandwidth by minimizing the Generalized Cross Validation (GCV). By model the annual inflation data (Indonesia) December 2006 - December 2011, the inflation target in 2012 is (4,5 + 1 )% can be achieved both exactly and predictly, while the inflation target in 2013 is (4,5 + 1 )% cannot be achieved neither exactly nor predictly. The inflation target in 2013 canâ€™t be achieve because since the beginning of 2013, there was a government policy to raise the price of electricity and the middle of 2013, there was an increase in fuel prices. The prediction of Indonesia inflation in 2014 by Gauss kernel is 6,18%. Keywords: Inflation, Kernel Regression Models, Generalized Cross Validation

KOMPARASI METODE PERAMALAN AUTOMATIC CLUSTERING TECHNIQUE AND FUZZY LOGICAL RELATIONSHIPS DENGAN SINGLE EXPONENTIAL SMOOTHING Endaryati, Betik; Kurniawan, Robert
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Automatic clustering technique and fuzzy logical relationships(ACFLR) is one of the forecasting method that used to predict time series data that can be applied in any data. Several previous studies said that this method has a good accuracy. Therefore, this study aims to compare the ACFLR methods with single exponential smoothing method and apply it to simulation data with uniform distribution. The performance of the method is measured based on MSE and MAPE. The results of the comparison of the methods showed that ACFLR has a higher forecasting accuracy than single exponential smoothing. This is evidenced by the value of MSE and MAPE of ACFLR is lower than single exponential smoothing.Keywords: Fuzzy, Forecasting, Automatic Clustering-Fuzzy Logic Relationships, Single Exponential Smoothing

PEMERIKSAAN DATA BERPENGARUH DALAM MODEL REGRESI GAMMA Hajarisman, Nusar
MEDIA STATISTIKA Vol 3, No 2 (2010): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In certain cases, often encountered a group of data where the observed respone variable shaped non-negative and not symmetrical (skewed to the right). The case data such as this one can be found in the field of insurance, such as variable stating the amount of claims or states of time between when a customer claims to obtain these claims. To handle such cases one of them is by using a generalized linear model, with respone variables that follow the gamma distribution. The paper discusses the inspection data outliers and influential data in the modeling of the respone following the gamma distribution. Several statistical measures used to examine the outlier data is the value of leverage, standardized deviance residual, standardized Pearson residual, and residual likelihood. Then the data outliers potentially influential data will be examined using Cook's distance statistics. Â Keywords: Gamma Distributions, Leverage, Standardized Deviance Residual, Standardized Pearson Residual, And Residual Likelihood, Cook's Distance Statistics

ANALISIS KEMISKINAN DI KABUPATEN MALUKU TENGGARA BARAT MENGGUNAKAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) Ferry Kondo Lembang; Henry Willyam Michel Patty; Feros Maitimu
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Poverty is a condition where there is a condition where there is an inability of the community to meet basic needs such as food, clothing, shelter, education and health. MTB regency is one of the regions in Moluccas Province with a relatively high percentage of the poor population reaching 28.31%. The purpose of this study is to conduct poverty analysis in MTB using the MARS method. The problem of poverty is thought to be very much influenced by many factors, therefore the selection of the MARS method is considered very appropriate because it has the advantage of being able analyze high-dimensional data. The results showed the best MARS model was a combination BF=18, MI=3 and MO=0 with a minimum GCV value at 69.587. Variables that have a significant effect are the percentage RTM that do not have public toilet facilities (X5), the variable percentage of RTM that is the type of floor of a residential building made of poor quality soil / bamboo / wood (X4), and the percentage of RTM that does not own the building (X1).

ANALISIS DATA INFLASI DI INDONESIA MENGGUNAKAN MODEL REGRESI SPLINE Suparti, Suparti
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. The developed model of parametric to cope with the volatility of the data is the ARCH and GARCH models. This alternative parametric models still requires the normality assumption in the data that often cannot be satisfied by financial data. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This research aims to conduct a study in Indonesia inflation data modeling using nonparametric methods is spline regression model with truncated spline bases. Goodness of a spline regression model is determined by an orde and knots location . However, the knots location are more dominant in spline regression model. One way to get the optimal knots location are by minimizing the value of Generalized Cross Validation (GCV). By modeling the annual inflation data of Indonesia in December 2006 - December 2011, the inflation target in 2012 is 4.5% + 1% can be achieved while the inflation target in 2013 is 4.5% + 1% cannot be achieved, because that prediction in 2013 is 8.55%. It was caused by government policy to raise the price of basic electricity and the fuel prices in 2013. Keywords : Inflation, Spline Regression Model, Generalized Cross Validation.

ANALISIS DATA INFLASI INDONESIA MENGGUNAKAN MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DENGAN PENAMBAHAN OUTLIER Suparti, Suparti; Sa'adah, Alfi Faridatus
MEDIA STATISTIKA Vol 8, No 1 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The inflation data is one of the financial time series data which often has high volatility. It is caused by the presence of outliers in the data. Therefore, it is necessary to analyze forecasting that can make all the assumptions are fulled without having to ignore the presence of outliers. The aim of this study is analyzing the inflation data in Indonesia using ARIMA model with the outlier detection. By modeling annual inflation data in December 2006 to December 2013 there are two types of outlier that are additive outlier (AO) and level shift (LS) outlier. The results show that The ARIMA model with the addition of outlier are better than the ARIMA model without outlier. The ARIMA ([1.12], 1.0) model with the addition of 19 outliers meet to the all assumptions that are the significance parameters, normality, homoscedasticity, and independence of residuals as well as the smallest MSE value.Â Keywords: Inflation, ARIMA, Outlier, MSE

ANALISIS DISKRIMINAN BERGANDA DENGAN PEUBAH BEBAS CAMPURAN KATEGORIK DAN KONTINU PADA KLASIFIKASI INDEKS PRESTASI KUMULATIF MAHASISWA Nur Walidaini; Moch. Abdul Mukid; Alan Prahutama; Agus Rusgiyono
MEDIA STATISTIKA Vol 10, No 2 (2017): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multiple discriminant analysis is one of the discriminant analysis techniques where the dependent variable are grouped into more than two groups. This paper discussed how to categorize Grade Point Average (GPA) of undergraduate student of Faculty of Sciences and Mathematics Diponegoro University based on categorical and continuous independent variable including gender, internet usage, time per week for learning, average score in national examination, amount of pocket money per month and the way to enter to Diponegoro University. The GPA grouping refers to the Academic Regulations of Diponegoro University i.e. satisfactory GPA (2,00 to 2,75), very satisfactory (2,76 to 3,50) and with honors (cum laude) (3,51 to 4,00). By using the multiple discriminant analysis with mixture variables, the accuration of classification based on training and testing data reach to 71,875% and 41,667% respectively.

PENDUGAAN DATA HILANG DENGAN MENGGUNAKAN DATA AUGMENTATION Nova, Mesra; Mukid, Moch. Abdul
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Data augmentation is a method for estimating missing data. It is a special case of Gibbs sampling which has two important steps. The first step is imputation or I-step where the missing data is generated based on the conditional distributions for missing data if the observed data are known. The next step is posterior or P-step where the estimation process of parameter values â€‹â€‹from the complete data is conducted. Imputation and posterior steps on the data augmentation will continue to run until the convergence is reached. The estimate of missing data is obtained through the average of simulated values. Â Keywords: Missing Data, Data Augmentation, Imputation Step, Posterior Step

KLASIFIKASI KEMISKINAN DI KOTA SEMARANG MENGGUNAKAN ALGORITMA CHISQUARE AUTOMATIC INTERACTION DETECTION (CHAID) DAN CLASSIFICATION AND REGRESSION TREE (CART) Dwi Ispriyanti; Alan Prahutama; Mustafid Mustafid; Tarno Tarno
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Decreasing poverty level is the first goal of Sustainable Development Goals (SDGs). Poverty in Central Java from 2002 to 2017 has decreased, as well as the city of Semarang. Therefore, it is necessary to examine the factors that determine the decline in poverty classification in the city of Semarang. The classification analysis in statistics uses one classification tree. Several methods using classification trees include CART, CHAID, C45 and ID3 algorithms. In this study the methods used were CART and CHAID Algorithms. CART and CHAID algorithms are binary classification trees. The CART separation rules use split goodness op, while CHAID uses CHI-Square. In the analysis, the value of using CART was 95.2% while CHAID was 95.2%. While the factors that influence poverty classification using CHAID include the acceptance of poor rice, the main building materials of the house walls, and the main fuel for cooking. Whereas with the CART Algorithm the variables that influence are the main fuels for cooking, poor rice receipts, the number of household members, final disposal sites, sources of drinking water, the household head's business field, roofing materials, and building walls.

KAJIAN ESTIMASI-M IRLS MENGGUNAKAN FUNGSI PEMBOBOT HUBER DAN BISQUARE TUKEY PADA DATA KETAHANAN PANGAN DI JAWA TENGAH Pradewi, Elen Dwi; Sudarno, Sudarno
MEDIA STATISTIKA Vol 5, No 1 (2012): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Ordinary Least Squares (OLS) is one method of parameter estimation in regression analysis. However, the presence of outliers can cause estimation of regression coefficients obtained are not exact. Act of throwing away an outlier is not a wise move, because sometimes outliers provide significant information. Therefore, robust regression methods are needed to data contain outliers. This paper will use robust regression estimation method by M-estimation. This estimation use Iteratively Reweighted Least Squares (IRLS) method with weighting function by Huber and Tukey Bisquare. IRLS is applied to the case of food security in Central Java in 2007 that is influenced by the stock of rice, harvested area, average production, price of rice and the amount of consumption. The purpose of this writing is to compare goodness of M-estimation IRLS using Huber and Tukey Bisquare function in estimating the model parameters of food security in Central Java in 2007. Based on the research results can be concluded that the M-estimation by the Tukey Bisquare is better recommended than Huber function. This can be seen by value results of Mean Square Error and determination coefficient

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