Jurnal Gaussian
Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
Articles
733 Documents
<![CDATA[PEMODELAN LAJU INFLASI DI PROVINSI JAWA TENGAH MENGGUNAKAN REGRESI DATA PANEL ]]>
<![CDATA[Dody Apriliawan]]>;
<![CDATA[Tarno Tarno]]>;
<![CDATA[Hasbi Yasin]]>
<![CDATA[ Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (465.378 KB)
|
DOI: 10.14710/j.gauss.v2i4.3791
<![CDATA[Panel regression is a regression which is a combination of cross section and time series. To estimate the panel regression there are 3 approaches, the common effect model (CEM), the fixed effect model (FEM) and the random effect model (REM). In the CEM, the parameters were estimated using the Ordinary Least Square (OLS). In the FEM, the parameters estimated by OLS through the addition of dummy variables. At REM, error is assumed random and estimated by the method of Generalized Least Square (GLS). This study aims to analyze the factors that influence inflation in the Central Java province using panel regression. Based on test result of panel regression, the appropriate model is the CEM. The parameters of model are estimated by using OLS the cross section weights. The model show that the Consumer Price Index (CPI), Minimum Salary of City/Regency (MSCR) and the economic growth significantly effect on percentage of inflation in Central Java Province.]]>
<![CDATA[PEMBENTUKAN PORTOFOLIO SAHAM DENGAN METODE MARKOWITZ DAN PENGUKURAN VALUE AT RISK BERDASARKAN GENERALIZED EXTREME VALUE (Studi Kasus: Saham Perusahaan The IDX Top Ten Blue 2017) ]]>
<![CDATA[Ria Epelina Situmorang]]>;
<![CDATA[Di Asih I Maruddani]]>;
<![CDATA[Rukun Santoso]]>
<![CDATA[ Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (459.802 KB)
|
DOI: 10.14710/j.gauss.v7i2.26655
<![CDATA[In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and level of trust. The characteristics of financial data are the extreme values that are alleged to have heavy tail and cause financial risk to be very large. The existence of extreme values can be modeled with Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio consisting of two stocks, namely a combination of TLKM and BMRI stocks for the best weight of 20%: 80% with the expected return rate of 0.00111 and standard deviation of 0.01057. Portfolio performance as measured by the Sharpe index is 1,06190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0,01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0,0206 or 2,06% of the current assets. Keywords: Portfolio, Risk, Heavy Tail, Value at Risk (VaR), Markowitz, Sharpe Index, Generalized Extreme Value (GEV).]]>
<![CDATA[PEMODELAN KURS RUPIAH TERHADAP DOLLAR AMERIKA SERIKAT MENGGUNAKAN REGRESI PENALIZED SPLINE BERBASIS RADIAL ]]>
<![CDATA[Kartikaningtiyas Hanunggraheni Saputri]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Abdul Hoyyi]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (480.219 KB)
|
DOI: 10.14710/j.gauss.v4i3.9477
<![CDATA[Exchange rate is the price of a currency from a country that is measured or expressed in another country's currency. A country's currency exchange rate has fluctuated due to exchange rate determined by the demand and supply of the currency. One of method that can be used to predict the exchange rate is the classical time series analysis (parametric). However, the data exchange rate that fluctuates often do not fulfill the parametric assumptions. Alternative used in this research is penalized spline regression which is nonparametric regression and not related to the assumption of regression curves. Penalized spline regression is obtained by minimizing the function Penalized Least Square (PLS). To handle the numerical instability and changing data then used radial basis at Penalized spline estimator. Selection of the optimal models is rely heavily on determining the optimal lambda and optimal knot point that is based on the Generalized Cross Validation (GCV) minimum. Using data daily exchange rate of the rupiah against the US dollar in the period of June 2, 2014 until February 27, 2015, the optimal penalized spline bases on radial model in this study is when using 2 order and 13 knots point, those points are 11625; 11669; 11728; 11795; 11911; 11974; 12069; 12118; 12161; 12372; 12452; 12550; 12667 with GCV = 3904.8.Keywords: exchange rate, penalized spline, radial bases, penalized least square, generalized cross validation]]>
<![CDATA[GEOGRAPHICALLY WEIGHTED REGRESSION PRINCIPAL COMPONENT ANALYSIS (GWRPCA) PADA PEMODELAN PENDAPATAN ASLI DAERAH DI JAWA TENGAH ]]>
<![CDATA[Nurmalita Sari]]>;
<![CDATA[Hasbi Yasin]]>;
<![CDATA[Alan Prahutama]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (697.565 KB)
|
DOI: 10.14710/j.gauss.v5i4.14728
<![CDATA[Linear Regression Analysis is a method for modeling the relation between a response variable with two or more independent variables. Geographically Weighted Regression (GWR) is a development of the regression model where each observation location has different regression parameter values because of the effects of spatial heterogenity. Regression Principal Component Analysis (PCA) is a combination of PCA and are used to remove the effect of multicolinearity in regression. Geographically Weighted Regression Principal Component Analysis (GWRPCA) is a combination of PCA and GWR if spatial heterogenity and local multicolinearity occured. Estimation parameters for the GWR and GWRPCA using Weighted Least Square (WLS). Weighting use fixed gaussian kernel function through selection of the optimum bandwidth is 0,08321242 with minimum Cross Validation (CV) is 3,009035. There are some variables in PCA that affect locally-generated revenue in Central Java on 2012 and 2013, which can be represented by PC1 that explained the total variance data about 71,4%. GWRPCA is a better model for modeling locally-generated revenue for the districts and cities in Central Java than RPCA because it has the the smallest Akaike Information Criterion (AIC) and the largest R2. Keywords : Spatial Heterogenity, Local Multicolinearity, Principal Component Analysis, Geographically Weighted Regression Principal Component Analysis.]]>
<![CDATA[ANALISIS VARIANSI PADA RANCANGAN BUJUR SANGKAR YOUDEN DENGAN DUA DATA HILANG ]]>
<![CDATA[Amalina Sari Dewi]]>;
<![CDATA[Tatik Widiharih]]>;
<![CDATA[Rita Rahmawati]]>
<![CDATA[ Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (680.581 KB)
|
DOI: 10.14710/j.gauss.v8i3.26680
<![CDATA[Youden Square Design (YSD) is an incomplete latin square design with at least one row/column which can’t run in an experiment. In this research we took 5x4 YSD (one column is not runned in an experiment). This design has a balance characteristic from a balanced incomplete block design where all treatments appears with the same number in each row. Missing data can occur in YSD. In this discussion, YSD with two missing data was used. Missing data is estimated by an iterative method then we arrange analysis of variance and LSD test. Analysis of variance with two missing data in YSD is calculated by adjusting the treatment sum of squares with it’s bias value and the total degrees of freedom and error degrees of freedom are substracted by two. LSD test is carried out if the treatment has a significant effect to the response. To clarify the discussion in YSD, example of application in the field of industry is given by observing the effect of the assembly method to the length of assembly time of X component. The assembly method has an effect to the length of assembly time of X component and if the missing data are and so the suggested assembly method is E method because it has the fastest average assembly time. Keywords: YSD, Missing Data, Analysis of Variance, LSD Test]]>
<![CDATA[ANALISIS PROCRUSTES PADA INDIKATOR INDEKS PEMBANGUNAN MANUSIA (IPM) DI KABUPATEN/KOTA PROVINSI JAWA TENGAH (STUDI KASUS IPM TAHUN 2008 DAN 2013) ]]>
<![CDATA[Bunga Maharani]]>;
<![CDATA[Moch. Abdul Mukid]]>;
<![CDATA[Suparti Suparti]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (584.675 KB)
|
DOI: 10.14710/j.gauss.v4i4.10129
<![CDATA[Human Development Index (HDI) as a measure of development in the performance of a whole formed through the approach of the four indicators which is real expenditure per capita, the average length of school, literacy rates and life expectancy. To learn about how the in contributing each indicators need to be identified the changes that occurred. Of the changes can use these as an ingredient of analysis in order to cope with or reduce the problems of development to realize the quality sustainably. Hence, this study aims to know of the changes of HDI in Central Java by mapping position of districts there are into a map geometry resulting from metric multidimensional scaling analysis. Where in 2008 as the beginning of leadership and 2013 as the end of leadership of Provincial Governor of Central Java Mr. Bibit Waluyo for five years served. By using an analysis procrustes, obtained the results that in the early period and the end of having the consistency of 90,53 %. In other words, the similarity of the large this indicates that at the beginning and end leadership of relatively no change.Keywords: Indicator, Map geometry, Metric multidimensional scaling, Changes analysis procrustes. ]]>
<![CDATA[ESTIMASI PARAMETER DISTRIBUSI WEIBULL DUA PARAMETER MENGGUNAKAN METODE BAYES ]]>
<![CDATA[Indria Tsani Hazhiah]]>;
<![CDATA[Sugito Sugito]]>;
<![CDATA[Rita Rahmawati]]>
<![CDATA[ Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (431.31 KB)
|
DOI: 10.14710/j.gauss.v1i1.578
<![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.]]>
<![CDATA[ANALISIS DATA RUNTUN WAKTU MENGGUNAKAN METODE WAVELET THRESHOLDING DENGAN MAXIMAL OVERLAP DISCRETE TRANSFORM ]]>
<![CDATA[Dyah Ayu Kusumaningrum]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Di Asih I Maruddani]]>
<![CDATA[ Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (695.996 KB)
|
DOI: 10.14710/j.gauss.v6i1.16132
<![CDATA[Wavelet is a mathematical tool for analyzing time series data. Wavelet has certain properties one of which is localized in the time domain and frequency and form an orthogonal basis in the space L2(R). There are two types of wavelet estimators are linear and nonlinear wavelet estimators. Linear wavelet estimators can be analyzed using the approach of Multiresolution Analysis (MRA), while nonlinear wavelet estimator called Wavelet Thresholding. Wavelet thresholding are emphasizing the reconstruction of wavelet using a number of the largest coefficient or can be said that only coefficient greater than a value taken, while other coefficients are ignored. There’re several factors that affect the smooth running of the estimation are the type of wavelet function, types of functions of thresholding, thresholding parameters, and the level of resolution. Therefore, in this thesis will have optimal threshold value in analyzing the data. Wavelet Thresholding method provides value of Mean Square Error (MSE) that smaller compare to wavelet method with the approach Multiresolution Analysis (MRA). In this case study Wavelet Thresholding are considered better in the analysis of time series data. Keywords: Multiresolution Analysis, Wavelet Thresholding Estimator.]]>
<![CDATA[DIAGRAM KONTROL MULTIVARIAT BERDASARKAN JARAK CHI-KUADRAT UNTUK QUALITY CONTROL PRODUKSI DI PT ARA SHOES ]]>
<![CDATA[Galuh Ayu Prameshti]]>;
<![CDATA[Sudarno Sudarno]]>;
<![CDATA[Diah Safitri]]>
<![CDATA[ Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (391.259 KB)
|
DOI: 10.14710/j.gauss.v3i4.8079
<![CDATA[Shoes are demands required by everyone. As a time changing and increasing demand for shoes, so many competitor shoe factories produce the best shoes for the customer. PT Ara Shoes is a famous shoe factory that has been well known for six decades. To be able to make fairness quality competition shoe factory would have to ability to produce a high quality product. To improve quality and production process is the way to determine whether quality of production is already achieve the minimum standard quality needed by applying the minimum standard quality control system. Control charts based on chi-square distance is a diagram of the control that can be used for multivariate data attributes. Production processes at PT ARA Shoes is divided into 3 stages of the shoe production process, including the process of cutting, process of sewing and assembling process. The cases study examined in this observation is the production process of cutting from January 2012 - October 2013 total applying 22 observations. Based on the research that has been done it is concluded that the production process is not enough controlled in cutting and improvement needed to be done twice, by eliminating observations 4th and 5th.Keywords : shoes charts control, chi-square distance, PT ARA Shoes]]>
<![CDATA[KLASIFIKASI PENERIMA PROGRAM BERAS MISKIN (RASKIN) DI KABUPATEN WONOSOBO DENGAN METODE SUPPORT VECTOR MACHINE MENGGUNAKAN LibSVM ]]>
<![CDATA[Yogi Setiyo Pamuji]]>;
<![CDATA[Diah Safitri]]>;
<![CDATA[Alan Prahutama]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (338.209 KB)
|
DOI: 10.14710/j.gauss.v4i4.10244
<![CDATA[Beras Miskin (Raskin) Program is a program of social protection, as supporters of other programs such as nutrition improvement, healthy increase, education and productivity improvement of Poor Households. According to Badan Pusat Statistika, there were 14 criteria to determine a household is classified as poor households. Based on these criteria it will be classified of recipient households and non-recipient households of Beras Miskin (Raskin) Program by Support Vector Machine (SVM) method using LibSVM. The concept of classification by SVM is search for the best hyperplane which serves as a separator of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow a separation. LibSVM is a package program created by Chih-Chung Chang and Chih-Jen Lin from Department of Computer Science at National Taiwan University. The method used by LibSVM to obtain global solution of duality lagrange problem is decomposition method. To determine the best parameters of kernel function, used k-vold cross validation method and grid search algorithm. In this classification by SVM method using LibSVM, obtain the best accuracy value as 83,1933%, which is the kernel function Radial Basis Function (RBF). Keywords : Beras Miskin (Raskin) Program, Classification, Support Vector Machine (SVM), LibSVM, Kernel Function]]>
