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Jurnal Gaussian

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Published by Universitas Diponegoro

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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.

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Articles 733 Documents

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PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (Aplikasi pada Data Harga Beras Kualitas Rendah di Indonesia Periode Januari 2013 – Februari 2019) Khusnul Umi Fatimah; Tarno Tarno; Abdul Hoyyi
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting

PEMODELAN PERTUMBUHAN EKONOMI JAWA TENGAH MENGGUNAKAN PENDEKATAN LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) Feby Kurniawati Heru Prabowo; Yuciana Wilandari; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The economic growth recently become more important because of its implementation widely, the economic growth concept is a measure of country or regional economy valuation. The economic growth data in this research that is measured by Gross Regional Domestic Product (GRDP) are susceptible of multicollinearity. Multicollinearity become a problem in regression analysis, especially in Ordinary Least Square (OLS) because it causes the regression coefficient estimates become not efficient. One of method to overcome multicollinearity is using Least Absolute Shrinkage and Selection Operator (LASSO). LASSO is a shrinkage method to estimate regression coefficients by minimazing residual sum of squares subject to a constraint. Because of that constraint, LASSO can shrinks coefficients towards zero or set them to exactly zero so it can do variable selection too. Based on Variance Inflation Factor (VIF), there are high correlations between predictor variables, so there is multicollinearity in growth economic data of Jawa Tengah 2013 if we use OLS. In this research, LASSO shrinks eleven coefficients estimator of predictor variables to exactly zero, so that variables considered to have not a significant influence toward model. Keywords : LASSO, Multicollinearity, Shrinkage, Gross Regional Domestic Product (GRDP)

PENDEKATAN SISTEM PERSAMAAN SIMULTAN DALAM PEMODELAN PRODUK DOMESTIK REGIONAL BRUTO (PDRB) PROPINSI JAWA TENGAH TAHUN 2000-2010 Rizky Oky Ari Satrio; Tatik Widiharih; Abdul Hoyyi
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Gross Domestic Product (GDP) is general indicator used to identify the economical development in a region. The condition of economy in Central Java Province is categorized as stable condition since it has GDP value developed rapidly year by year. Refer to model used by Bappenas,the simultaneous equation model between GDP is influenced by number of employee and government spending.Identification of the model in this study using the ordercondition of indetification on the basis of the result of the overidentified taken the GDP of agriculture, mining, electricity, gas and water sector and trade. Therefore, the parameter evaluation used is 2SLS method (Two Stage Least Square). After fulfilled assumption of independent, identical and normal distribution, the most valued toward model of GDP in Central Java Province is GDP sector of agriculture.

PEMODELAN FIXED EFFECT GEOGRAPHICALLY WEIGHTED PANEL REGRESSION UNTUK INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH Siti Maulina Meutuah; Hasbi Yasin; Di Asih I Maruddani
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Human development index is an indicator for assessing the quality of human resources and measure the results of human development. The achievements of the human development index is not enough if conducting observations in each cities in just one particular time, but the observations need to be made in some period of time. The distribution in each cities is also a concern, because the conditions are so diverse that led to their spatial effects. Therefore, it is necessary to study these variables in some time periods that affect human development index taking into account the spatial effects. Statistical methods used to overcome their spatial effects, especially in the problem of spatial heterogeneity in the data type of panel is Geographically Weighted Panel Regression (GWPR). This study focused on the establishment of GWPR model with fixed effects using fixed exponential kernel on the human development index data cities in Central Java in 2010-2015. The results of this study indicate that the fixed effect model GWPR differ significantly on panel data regression model, and the model generated for each location will be different from one another. In addition, cities in Central Java has five groups based on variables that are significant. In the fixed effect model GWPR generates R2 value of 92.27%.Keywords: Human Development Index, Panel Data, Spatial Effects, Fixed Effect, Fixed Exponential Kernel, Geographically Weighted Panel Regression, R2.

PEMODELAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) Ndaru Dian Darmawanti; Suparti Suparti; Diah Safitri
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Composite Stock Price Index (CSPI) is a historical information about the movement of joint-stock until a certain date. CSPI is often used by inventors to see a representation of the overall stock price, it can analyze the possibility of increase or decrease in stock price. Following old examination, some economy macro variables affecting CSPI is inflation, interest rate,and exchange rate the Rupiah againts the u.s.dollar. MARS method is particularly suitable to analyze a CSPI because many variables that affected. Furthermore, in the real world is very difficult to find a spesific data pattern. The analysis is MARS analysis. The purpose is an obtained a MARS model to be used to analyze the CSPI movement’s. Selection MARS model can be used CV method. The MARS model is an obtained from combination of BF, MI, dan MO. In this case, happens the best models with BF=9, MI=2, dan MO=1. Accuracy for MARS model can see MAPE values is 14,32588% it means the model can be used.Keyword: CSPI, economy macro, MARS, CV, MAPE.

PENDUGAAN AREA KECIL TERHADAP PENGELUARAN PER KAPITA DI KABUPATEN SRAGEN DENGAN PENDEKATAN KERNEL Bitoria Rosa Niashinta; Dwi Ispriyanti; Abdul Hoyyi
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Data of Social Survey and Economic National is a relatively small sample of data, so that data is called small area. Estimation of parameter in small area can be done in two ways, there are direct estimation and indirect estimation. Direct estimation is unbias estimation but give a high variance because from small sample of data. The technique that use to increase efectivity of sample size is indirect estimation or called Small Area Estimation (SAE). SAE is done by adding auxiliary variable. on estimating parameter. Assumed that auxiliary variable has a linear correlation with the direct estimation. If that assumption is incomplete, use an nonparametric approaching. This research is using Kernel Gaussian approaching to build a correlation between direct estimation which expenditure per capita and auxiliary variable which population density. Evaluation of estimation result is done by comparing the value of direct estimation variance with the value of indirect estimation variance using Kernel Gaussian approaching. The result of parameter estimation which approached by SAE is the best estimation, because it produce the small value of variance that is 5,31275, while the value of direct estimator variance is 6,380522. Keywords : Direct Estimation, Small Area Estimation (SAE), Kernel Gaussian

PENDUGAAN DATA HILANG PADA RANCANGAN ACAK KELOMPOK LENGKAP DENGAN ANALISIS KOVARIAN Vina Riyana Fitri; Triastuti Wuryandari; Diah Safitri
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Analysis of Covariance (ANCOVA) is mostly used in the analysis of research or experimental design. ANCOVA is the combination between regression analysis and Analysis of Variance (ANOVA). ANCOVA were used because there are some concomitant variable, which is variable that difficult to control by the researchers but an impact on observed the response variable. The purpose from concomitant variable is reduces variability in the experiment. If there is missing data on Randomized Complete Block Design (RCBD) the first must be done estimating the missing data before ANCOVA done. ANCOVA on RCBD with complete data or missing data isn’t much different, if there are missing data, the degrees of freedom is reduced by the total amount of missing data and the sum of square treatment reduced by the value of the bias. Application of tensile strength of the glue experiment to the case ANCOVA on RCBD with one missing data show no effect of treatment and group by the tensile strength of the glue. For Fe toxicity experiment with two missing data are found only treatment effect to Fe texicity. Based on value from the coefficient of variance for one missing data and two missing data showed that ANCOVA is more appropriately used than ANOVA.

ANALISIS PORTOFOLIO OPTIMAL MENGGUNAKAN MULTI INDEX MODEL (Studi Kasus: Kelompok Saham IDX30 periode Januari 2014 – Desember 2018) Bramadita Kunni Fauziyyah; Alan Prahutama; Sudarno Sudarno
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investment is the placement of a number of funds at this time in the hope of making a profit in the future. The purpose of investors investing is to get many profit by understanding that there is a possibility of losses. But, the higher the expected return then the risk also greater. The method to minimize risk is portfolio. One of the optimum portfolio method is Multi Index Model. Multi Index Model is model that use more than one index or factor that affects the return on stock. The stock in this research is 10 stocks of IDX30 period January 2014 – December 2018. Index in this research is IHSG, Hang Seng Index and DJIA. Multi Index Model has assumptions: residual variance of stock i equals , variance of index j equals , E(ci) = 0, covariance between index equals 0, covariance between the residual for stock and index equals 0 and covariance between the residual for stock equals 0. The result of this research is there are 4 stocks that fulfill the assumpions to be made as the optimum portfolio, that is GGRM (Gudang Garam Tbk) 23.67%, UNVR (Unilever Indonesia Tbk) 37.09%, BBCA (Bank Central Asia Tbk) 25.15% dan ASII (Astra International Tbk) 14.09% with a value of expected return portfolio is 1.19% and risk of portfolio is 3.79%. Keywords: Investment, Optimum Portfolio, Multi Index Model

PERBANDINGAN ANALISIS KLASIFIKASI NASABAH KREDIT MENGGUNAKAN REGRESI LOGISTIK BINER DAN CART (CLASSIFICATION AND REGRESSION TREES) Agung Waluyo; Moch. Abdul Mukid; Triastuti Wuryandari
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is the greatest asset managed by the bank and also the most dominant contributor to the bank's revenue. Debtor to pay credit to the bank may smoothly or jammed. There is a relationship variables that affect a debtor smoothly or jammed in paying credit. This study aims to identify the variables that affect a debtor's credit status. The variables used in this study were gender, education level, occupation, marital status and income. Analytical methods used include Binary Logistic Regression analysis and CART (classification and regression trees). Classification accuracy of the two methods will be compared. Based on the research results of binary logistic regression showed that the variables that affect the debtor's credit status is revenue with 80% classification accuracy. While the results of CART (classification and regression trees) in the form of a decision tree shows the type of work chosen as the root node spliting, with a classification accuracy of 81%. Keywords: credit status, logistic regression, CART

PENERAPAN METODE STRUCTURAL EQUATION MODELING UNTUK ANALISIS KEPUASAN PENGGUNA SISTEM INFORMASI AKADEMIK TERHADAP KUALITAS WEBSITE (Studi Kasus pada Website sia.undip.ac.id) Enggar Nur Sasongko; Mustafid Mustafid; Agus Rusgiyono
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Quality of website has an important role in giving effect to the website user's satisfaction. The quality of a website is measured by the adjusted WebQual dimensions include the dimensions of the system, dimension of information, dimension of interaction and dimension of services. Structural Equation Modeling is a method that used to examine complicated correlation simultaneously consisting of dependent variables and independent variables. This research aims to apply Structural Equation Modeling and Importance Performance Analysis methods in determining the influence of website quality factors on user satisfaction of academics Information System's website, and to find the performance of variables that need to be improved. This research is conducted at the University of Diponegoro, involving 200 students from Diponegoro University as the respondents. From the test of overall models, it obtained Goodness of Fit with the value of Chi Square = 68.748 and RMSEA = 0.084. From the analysis, it can be concluded that the dimension of interaction has the effect of 35%, dimension of information in amount of 35%, the dimension of service is 22.1%, and the dimensions of system in amount of 8.7%. And variables that need to improve performance are ease of website to be accessed's variable, variable of detail information, and ease of PBM evaluation's variable. Keywords: website quality, user satisfaction, Structural Equation Modeling

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Jawa tengah

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