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Media Statistika
Published by Universitas Diponegoro
ISSN : -     EISSN : 24770647     DOI : -
Core Subject : Science,
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Articles 187 Documents
KEEFEKTIFAN PRAUJIAN NASIONAL MATEMATIKA TAHUN AKADEMIK 2004/2005 (Studi Kasus di SMK Negeri dan Swasta di Jakarta Selatan 06) Hoyyi, Abdul
MEDIA STATISTIKA Vol 2, No 1 (2009): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (284.963 KB) | DOI: 10.14710/medstat.2.1.29-38

Abstract

National pre-exam is one way of the evaluation to the student’s ability. Through national pre-exam, it would get information how far the student’s preparation to have national exam. National pre-exam is expected to improve student’s score on national exam. In addition, national pre-exam is expected can be used to evaluate student’s preparation and it can predict national examination score. The improving of student’s achievement depends on the way the analysis of change of national examination achievement distribution and description statistics analysis national examination score. The statistics of McNemar’s test is used to know student’s preparation, because the sample is dependent. Correlation and simple linier regression analysis used for analysis prediction. The increase of national examination score not always the effect of pre-national examination. The pre-national examination can’t be used to estimate student’s preparation. The probability student that pass the national exam is higher than pre-national exam. It is caused by pre-national exam is more difficult than national exam through the same passing limit. The score of national exam prediction is obtained confidence limit wide enough. Therefore, the variant national of examination achievements is quite large.  Key words: National Pre-exam, National Exam, Description Analysis, McNemar’s Test; Predictionhttp://ejournal.undip.ac.id/index.php/media_statistika/article/view/2481
PEMODELAN REGRESI BERGANDA DAN GEOGRAPHICALLY WEIGHTED REGRESSION PADA TINGKAT PENGANGGURAN TERBUKA DI JAWA TENGAH Utami, Tiani Wahyu; Rohman, Abdul; Prahutama, Alan
MEDIA STATISTIKA Vol 9, No 2 (2016): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (303.285 KB) | DOI: 10.14710/medstat.9.2.133-147

Abstract

The problems in employment was the growing number of Open Unemployment Rate (OUR). The open unemployment rate is a number that indicates the number of unemployed to the 100 residents are included in the labor force. The purpose of this study is mapping the data OUR in Central Java and the suspect and identify linkages between factors that cause OUR in the District / City of Central Java in 2014. Factors that allegedly include population density (X1), Inflation (X2), the GDP value (X3), UMR Value (X4), the percentage of GDP growth rate (X5), Hope of the old school (X6), the percentage of the labor force by age (X7) and the percentage of employment (X8). Geographically Weighted Regression (GWR) is a method for modeling the response of the predictor variables, by including elements of the area (spatial) into the point-based model. This research resulted in the conclusion that the OLS regression models have poor performance because the residual variance is not homogeneous. There were no significant differences between GWR models with OLS model or in other words generally predictor variables did not affect the response variable (rate of unemployment in Central Java) spatially. However, GWR model could captured modelling in each region. Keywords: multiple linear regression, geographiically weighted regression, open unemployement rate in Central Java.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (606.598 KB) | DOI: 10.14710/medstat.5.1.1-10

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.866 KB) | DOI: 10.14710/medstat.12.1.63-72

Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.446 KB) | DOI: 10.14710/medstat.4.2.73-86

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.333 KB) | DOI: 10.14710/medstat.10.2.71-83

Abstract

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. 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.653 KB) | DOI: 10.14710/medstat.8.1.1-11

Abstract

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. Keywords: Inflation, ARIMA, Outlier, MSE
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (651.863 KB) | DOI: 10.14710/medstat.6.1.1-9

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (207.665 KB) | DOI: 10.14710/medstat.12.2.188-199

Abstract

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 EFISIENSI BANK PERKREDITAN RAKYAT DI KOTA SEMARANG DENGAN PENDEKATAN DATA ENVOLEPMENT ANALYSIS Septianto, Hendi; Widiharih, Tatik
MEDIA STATISTIKA Vol 3, No 1 (2010): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (198.573 KB) | DOI: 10.14710/medstat.3.1.41-48

Abstract

The research was conducted to measure rural banks (Bank Perkreditan Rakyat / BPR) efficiency level in Semarang city. The measurement was done using non parametric approach with Data Envolepment Analysis (DEA) method constant return to scale assumption (CCR model). The research was using all rural banks in Semarang  (16 rural banks). The result indicated that 6 rural banks were efficient and 10 rurals banks were inefficient.   Keywords: CCR Model, Efficient, Rural Bank

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