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<![CDATA[DIAGRAM KENDALI MEWMV DAN MEWMA BERBASIS MODEL TIME SERIES PADA DATA BERAUTOKORELASI: STUDI KASUS GULA KRISTAL PUTIH ]]>
<![CDATA[Novri Suhermi]]>;
<![CDATA[Retno Puspitaningrum]]>;
<![CDATA[Agus Suharsono]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.1.26-38
<![CDATA[In this study, we aim to build a multivariate control chart for autocorrelated data. We use MEWMA and MEWMV control charts which are free of normality assumption. Time series model is then applied to tackle autocorrelation problem in the data where the control charts require independence assumption. The real dataset used is the quality characteristics of white crystal sugar, also called gula kristal putih (GKP). There are 3 quality characteristics of GKP, namely moisture (%), color of solution (IU), and grain type (mm). It is considered that these quality characteristics are correlated each other. Our results show that the variability process is out of control where there are 5 observations outside the control limits. Meanwhile the mean process is also out of control. The factors causing the out of control include the workers, the raw materials, the measurement, the machines, and the methods. The process capability indices result in the values less than 1 which means the process is not sufficiently capable.]]>
<![CDATA[ESTIMASI PARAMETER DISTRIBUSI WEIBULL DUA PARAMETER MENGGUNAKAN METODE BAYES ]]>
<![CDATA[Hazhiah, Indria Tsani]]>;
<![CDATA[Sugito, Sugito]]>;
<![CDATA[Rahmawati, Rita]]>
<![CDATA[ MEDIA STATISTIKA Vol 5, No 1 (2012): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.5.1.27-35
<![CDATA[Interval estimation of a parameter is one part of statistical inference. One of the methods that used is the Bayes method. A Bayesian method is combine prior distribution and distribution of samples, so that the posterior distribution can be obtained. Interval estimation using a method Bayes called credibel interval estimation. In this thesis, the distribution of the sample is used a two-parameter Weibull distribution scale-shape-version of survival distribution (reliability). Data that used are data that is not censored data type and data type II censored if prior distribution using non-informative which of the produce distribution the resulting posterior distribution is gamma distribution. Parameters of the sample distribution that to find out is a parameter that  by the parameter c (shape parameter) known while the parameter b (scale parameter) had unknown. Keywords: Bayes Method, Two-Parameters Weibull Distribution , Gamma Distribution, The Estimated Credible Interval.]]>
<![CDATA[MODEL PENILAIAN KREDIT MENGGUNAKAN ANALISIS DISKRIMINAN DENGAN VARIABEL BEBAS CAMPURAN BINER DAN KONTINU ]]>
<![CDATA[Mukid, Moch. Abdul]]>;
<![CDATA[Widiharih, Tatik]]>
<![CDATA[ MEDIA STATISTIKA Vol 9, No 2 (2016): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.9.2.107-117
<![CDATA[Credit scoring models is an important tools in the credit granting process. These models measure the credit risk of a prospective client. This study aims to applied a discriminant model with mixed predictor variables (binary and continuous) for credit assesment. Implementation of the model use debitur characteristics data from a bank in Lampung Province which the used binary variables involve sex and marital status. Whereas, the continuous variables that was considered appropriate in the model are age, net income, and length of work. By using the data training, it was known that the misclassification of the model is 0.1970 and the misclassification of the testing data reach to 0.3753. Keywords: discriminant analysis, mixed variables, credit scoring]]>
<![CDATA[ESTIMASI REGRESI NON PARAMETRIK DENGAN METODE WAVELET SHRINKAGE NEURAL NETWORK PADA MODEL RANCANGAN TETAP ]]>
<![CDATA[Yasin, Hasbi]]>
<![CDATA[ MEDIA STATISTIKA Vol 2, No 1 (2009): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.2.1.1-10
<![CDATA[If X is a predictor variable and Y is a response variable of following model Y = g(X) +e with function g is a regression which not yet been known and e is an independent random variable with mean 0 and variant . The function of g can be estimated by parametric and nonparametric approach. In this paper, g is estimated by nonparametric approach that is named wavelet shrinkage neural network method. At this method, the smoothly function estimation is depending on shrinkage parameter’s that are threshold value and level of wavelet that be used. It also depending on the number of neuron in the hidden layer and the number of epoch that be used in feed forward neural network. Therefore, it is required to be select the optimal value of threshold, level of wavelet, the number of neuron and the number of epoch to determine optimal function estimation.  Keywords: Nonparametric Regression, Wavelet Shrinkage Neural Network]]>
<![CDATA[KAJIAN STATISTICAL DAN COST EFFICIENCY DALAM PENENTUAN GUGUS SAMPEL BLOK SENSUS TERBAIK (Studi Kasus: Sampling Design Susenas-2015 di Kabupaten Natuna) ]]>
<![CDATA[Wiwik Andriyani Lestari Ningsih]]>;
<![CDATA[I Made Arcana]]>
<![CDATA[ MEDIA STATISTIKA Vol 11, No 2 (2018): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.11.2.93-105
<![CDATA[Two aspects of efficiency that should be considered in applying sampling design of a survey are statistical efficiency and cost efficiency. Efficiency in statistical aspect improves precision of estimators obtained by the survey data, whereas efficiency in cost aspect provides an economic survey. The purpose of this researchis to evaluate the both efficiencies in all possible census blocks (CBs) sample setand to identify the best CBs sample set in the 2015 National Socio-Economic Survey (Susenas). Therefore, a computer program for calculating statistical, and cost efficiency aspects was developed in this research to determine the best sampel set of CBs among all possible sampel set of CBs based on sampling design of the 2015 Susenas implemented in Natuna District, Kepulauan Riau Province. The best possible sample set of CBs is determinedby considering statistical efficiency aspect, cost efficiency aspect, as well as combination of those two aspects. The result showed that the best sample set of CBs on statistical efficiency aspect provided the CBs sample set having minimum value of RSE index; evaluation on cost efficiency aspect provided the best CBs sample set having minimum value of total cost esimated using the total score of accessibility index; and evaluation on both efficiency aspects provided the best CBs sample set having minimum value of RSE index and minimum value of total score of accessibility index. Keywords: sampling design, all possible samples, statistical efficiency, cost efficiency]]>
<![CDATA[PROSES INFERENSI PADA MODEL LOGIT ]]>
<![CDATA[Rusgiyono, Agus]]>
<![CDATA[ MEDIA STATISTIKA Vol 1, No 2 (2008): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.1.2.91-98
<![CDATA[Let represent the response on a nominal random variable of Bernoulli distribution, with    ,  where is a parameter with unknown value. Problems of estimating used smallest square methods in linier regression model can overcome with used maximum likelihood method in logistic regression.. Suppose  is maksimum likelihood estimstors of . In case can be obtained from first condition, ln(Ln(p)) to be maximum at point then be obtained and that is unbiased estimator because To be test hipothesis that , with a large sample size used fact that  Keywords : Estimator, unbiased estimator, test statistic]]>
<![CDATA[GRAFIK PENGENDALI RAGAM SAMPEL UNTUK MONITORING VARIABILITAS PROSES PRODUKSI ]]>
<![CDATA[Sudarno, Sudarno]]>
<![CDATA[ MEDIA STATISTIKA Vol 7, No 1 (2014): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.7.1.11-19
<![CDATA[The control chart is a graphical display or a quality characteristic that has been measured or computed from a sample versus the sample number or time. The variance chart is used to monitoring variability of production process. It is an altenative way to check variability process rather than R chart or s chart. The problems will be done are find the parameters of variance chart, predict process capability, verify defect per million opportunities (DPMO) of process result and simulation kinds of shift sigma values. This result could be used as information to production process at the future time. The result of discussion that upper conrol limit = 0.0014, center line = 0.00073, lower control limit = 0.00028, process capability = 1.003 and DPMO = 2,620 part per million. These parameters used for information in the next production process. Keywords: Variance Chart, Process Capability, Defect per million opportunities, Shift.]]>
<![CDATA[TIME SERIES ANALYSIS USING COPULA GAUSS AND AR(1)-N.GARCH(1,1) ]]>
<![CDATA[Caraka, Rezzy Eko]]>;
<![CDATA[Yasin, Hasbi]]>;
<![CDATA[Sugiarto, Wawan]]>;
<![CDATA[Ismail, Kadi Mey]]>
<![CDATA[ MEDIA STATISTIKA Vol 9, No 1 (2016): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.9.1.1-13
<![CDATA[In this case, the Gaussian Copula is used to connect the data that correlates with the time and with other data sets. Most often, practitioners rely only on the linear correlation to describe the degree of dependence between two or more variables; an approach that can lead to quite misleading conclusions as this measure is only capable of capturing linear relationships. Correlation doesn’t mean causation, prediction using Copula is built on three things that the marginal distribution function, the kernel function, and the function of the Copula. Gaussian Copula involves the covariance matrix are approximated by using kernel functions. Kernel acts as the correlation between the approach of the data values that have the same characteristics. In this case, the characteristics used is the time. The advantage of the kernel function is able to calculate the correlation between random variables that have a realization using data characteristics. The advantage of using the kernel based Copula able to capture the dependencies between data and process data that have the same characteristics with time. Another benefit is that it allows a sequence of random variables have a joint distribution function so that the conditional probability of the prediction can be calculated. Keywords: Binding, Copula, GARCH, Gauss, Time Series]]>
<![CDATA[ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KESEMBUHAN PASIEN PENYAKIT FLU BURUNG ]]>
<![CDATA[Wilandari, Yuciana]]>;
<![CDATA[Safitri, Diah]]>
<![CDATA[ MEDIA STATISTIKA Vol 2, No 1 (2009): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.2.1.11-18
<![CDATA[Avian influenza is contagion which caused by influenza virus type H5N1 often cause death. Avian influenza anticipated to be influenced by gender, age, epidemiology and case, to know the factors have a significant effect used by independent test. is later on made model of regresi binary logistics. Then obtained by factor having an effect is case and epidemiology, that is made regression logistics model. Someone which including case of suspect to be able to have probability recover bigger than someone which including confirmation case, someone which contact with dead an avian to be able to have probability recover smaller than someone which no contact.]]>
<![CDATA[ANALISIS KORESPONDENSI UNTUK PEMETAAN PERSEPSI ]]>
<![CDATA[Rusgiyono, Agus]]>
<![CDATA[ MEDIA STATISTIKA Vol 3, No 2 (2010): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.3.2.117-123
<![CDATA[Correspondence analysis used to investigate the relationship between two or more qualitative variables. This technique could shrink the dimensions of variables and describe the profile vector of rows and columns of a matrix vector data from the contingency table. Target correspondence analysis is to show the relationship variables rows and columns as well as visualization variables in R2-dimensional space, using the Chi square of the distance definition in sub-Euclidean space. Keywords: Profile of Row and Column Vectors, Chi Square Distance, Euclidean Subset]]>
