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.
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<![CDATA[ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI UPAH MINIMUM KABUPATEN/KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN MODEL SPATIAL AUTOREGRESSIVE (SAR) ]]>
<![CDATA[Rahmah Merdekawaty]]>;
<![CDATA[Dwi Ispriyanti]]>;
<![CDATA[Sugito Sugito]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14709
<![CDATA[Spatial regression is the result of the development of linear regression method, wherein the location or spatial aspects of the analyzed data are also must be considered. The phenomenon that includes spatial data of which is the deployment of a minimum wage. Minimum Wages District/City is a minimum standard that is used by employers to provide wages to employees in its business environment on a district/city in any given year. Minimum Wages District/City is determined by considering the welfare of workers and the state of the local economy. Factors in worker welfare such as Worth Living Needs and the Consumer Price Index (CPI), while one important indicator to determine the economic conditions in the region within a certain time period is Gross Domestic Product (GDP). Modeling the influence of these factors can be determined by using multiple linear regression and spatial regression. Based on the data processing result, there is a spatial dependence in the Minimum Wages District/City variable in Central Java, so Spatial Autoregressive (SAR) method is used in this study. Variables that significantly affect the UMK in Central Java through multiple linear regression method and SAR is the Worth Living Needs (X1) and CPI (X2). The SAR model generates the value of R2 at 72.269% and AIC at 66.393, better than the multiple linear regression model that generates the value of R2 at 68% and AIC at 68.482.Keywords : Minimum Wages District/City, Worth Living Needs, CPI, GDP, multiple linear regression, spatial dependence, Spatial Autoregressive]]>
<![CDATA[PEMODELAN REGRESI SPLINE TRUNCATED UNTUK DATA LONGITUDINAL ( Studi Kasus : Harga Saham Bulanan pada Kelompok Saham Perbankan Periode Januari 2009 – Desember 2015 ) ]]>
<![CDATA[Khoirunnisa Nur Fadhilah]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Tarno Tarno]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14699
<![CDATA[Stocks are securities that can be bought and sold by individuals or institutions as a sign of ownership of any person nor bussines entity within a company. From the value of market capitalization, the stock is divided into 3 groups: large capitalization (big-cap), medium capitalization (mid-cap), and small capitalization (small-cap). The stocks has been fluctuated up and down because of several factors, one of them is inflation. Longitudinal data are observations made of n subjects that mutually independent with each subject which observed repeatedly in different period of time mutually dependent. Modelling longitudinal data of stock prices do with truncated spline nonparametric regression approach. The best model of spline depends on the determination of the optimal knot points which has minimum value of Generalized Cross Validation (GCV). The best of truncated spline regression is spline order 2 with 3 knot points for each of the subjects on longitudinal data. By using the model, the value of MAPE for each subject is 29,93% for PT Bank Mandiri (Persero) Tbk., 16,67% for PT Bank Bukopin Tbk., and 12,99% for PT Bank Bumi Arta Tbk.. Keywords: stocks, longitudinal data, truncated spline, GCV]]>
<![CDATA[KLASIFIKASI KEIKUTSERTAAN KELUARGA DALAM PROGRAM KELUARGA BERENCANA (KB) DI KOTA SEMARANG MENGGUNAKAN METODE MARS DAN FK-NNC ]]>
<![CDATA[Aryono Rahmad Hakim]]>;
<![CDATA[Diah Safitri]]>;
<![CDATA[Sugito Sugito]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14690
<![CDATA[Classification method is a statistical method for grouping or classifying data. A good classification method will produce a little bit of misclassification. Classification method has been greatly expanded and two of the existing classification methods are Multivariate Adaptive Regression Spline (MARS) and Fuzzy k-Nearest Neighbor in Every Class (FK-NNC). This study is aimed to compare a classification of Keluarga Berencana participation based on suspected factors that affect them between the methods of MARS and FK-NNC. This study uses secondary data which one is the participation of Keluarga Berencana in Semarang on 2014. Evaluation of errors use an Apparent Error Rate (APER). In the method MARS best classification results is obtained with the combination of BF = 24, MI = 3, MO = 0 for generating a smallest Generalized Cross Validation (GCV) value and the APER is obtained by 19%. While FK-NNC method is obtained the best classification results in k = 3 for generating the greatest accuracy of classification value and APER value is obtained by 22%. Based on APER (Apparent Error Rate) calculation, it shown that the classification of family participation in Keluarga Berencana (KB) programs in Semarang using MARS method is better than FK-NNC method.Keywords: Classification, MARS, FK-NNC, APER, Keluarga Berencana]]>
<![CDATA[ANALISIS DISKRIMINAN FISHER POPULASI GANDA UNTUK KLASIFIKASI NASABAH KREDIT ]]>
<![CDATA[Ungu Siwi Maharunti]]>;
<![CDATA[Moch. Abdul Mukid]]>;
<![CDATA[Agus Rusgiyono]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14714
<![CDATA[Credit is the biggest asset carried out by a bank and become the most dominant contributor to the bank income. However, the activity to distribute the credit takes a risk which can influence health and continuance of bank business. The credit risk which potentially occurs can be measured and controlled by analyzing directly whichever the credit client categorized to. The credit risk categorized to current credit, in specific concern credit, less current credit, doubtful credit and bad credit based on Bank Indonesia Regulation No.: 7/2/PBI/2005. The independent variables used in this research are nominal credit, principal balance, in time being bank client, time period, and bank interest. Fisher multiple discriminant analysis is a method whose assumption equality of covariance matrices. The result from using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati shows that variable principal balance, in time being bank client, time period, and bank interest significant to measure credit risk. The classification using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati gives the accurate 64,33%. Keywords: credit, classification, fisher multiple discriminant analysis]]>
<![CDATA[ANALISIS KINERJA PORTOFOLIO OPTIMAL DENGAN METODE MEAN-GINI ]]>
<![CDATA[Mega Susilowati]]>;
<![CDATA[Rita Rahmawati]]>;
<![CDATA[Alan Prahutama]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14705
<![CDATA[Investments in financial assets has a special attraction that investors can form a portfolio. Portfolio is investment which comprised of various stocks from different companies. A common issue is the uncertainty when investors are faced with the need to choose stocks to be formed into a portfolio of his choice. A rational investor, would choose the optimal portfolio. Determination of the optimal portfolio can be performed by various methods, one of which is a method of Mean-Gini. Mean-Gini is the expected value of the portfolio return is the weight density function while the random variable is the average of the shares. Mean-Gini methods used to generate the greatest value of portfolio return expectations with the smallest risk. Mean-Gini does not require the assumption of normality on stock returns. Mean-Gini was first introduced by Shalit and Yitzhaki in 1984. This research uses data of closing prices stocks from January 2008 to December 2015. Measurement of portfolio performance with Mean-Gini performed using the Sharpe index. Based on Sharpe index, the optimal portfolio is second portfolio with three stocks portfolio and the proportion investments are 25.043% for SMGR, 68.148% for UNVR and 6.809% for BMRI. Keywords: Stock, Portfolio, Mean-Gini, Sharpe index.]]>
<![CDATA[PENERAPAN METODE STRUCTURAL EQUATION MODELING UNTUK ANALISIS KEPUASAN PENGGUNA SISTEM INFORMASI AKADEMIK TERHADAP KUALITAS WEBSITE (Studi Kasus pada Website sia.undip.ac.id) ]]>
<![CDATA[Enggar Nur Sasongko]]>;
<![CDATA[Mustafid Mustafid]]>;
<![CDATA[Agus Rusgiyono]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14695
<![CDATA[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]]>
<![CDATA[PERBANDINGAN MODEL GWR DENGAN FIXED DAN ADAPTIVE BANDWIDTH UNTUK PERSENTASE PENDUDUK MISKIN DI JAWA TENGAH ]]>
<![CDATA[Pamungkas, Rifki Adi]]>;
<![CDATA[Yasin, Hasbi]]>;
<![CDATA[Rahmawati, Rita]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14710
<![CDATA[Regression analysis is statistical method for modeling the dependency relationship that might exist among the dependent variable with independent variable. Geographically Weighted Regression (GWR) is an expansion of linier regression model where each of the parameters from every observation sites is counted, so each sites have local regression parameter. Weighted Least Square (WLS) model is applied to estimate the parameter of GWR model. GWR method differentiates bandwidth kernel into two, fixed bandwidth kernel and adaptive bandwidth kernel. Fixed kernel has the same bandwidth in each observation location, meanwhile adaptive kernel has different bandwidth value in each observation location. Cross Validation (CV) is used to choose the most optimum bandwidth. The application of GWR model to show the percentage of poor population at district and city of Central Java shows that GWR model is significantly different in each location towards global regression model, also the estimated model will also give different result between one location and another. Based on Akaike Information Criterion (AIC) value between global regression models with GWR, it is know that GWR model with fixed exponentially weighted kernel is the best model to use to analyze the percentage poor population at district and city of Central Java because of it has the smallest AIC value. Keywords: Akaike Information Criterion, Bandwidth, Cross Validation, Exponential Kernel Function, Geographically Weighted Regression, Weighted Least Square]]>
<![CDATA[MODEL REGRESI COX STRATIFIED PADA DATA KETAHANAN ]]>
<![CDATA[Mohamad Reza Pahlevi]]>;
<![CDATA[Mustafid Mustafid]]>;
<![CDATA[Triastuti Wuryandari]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14701
<![CDATA[Stratified Cox model on the events are not identical is a modification of the Cox Proportional Hazard models when there are individuals who experienced more than one incident. This study aims to form a stratified Cox regression models for repeated occurrences of data are not identical and their application to cases of hemorrhagic stroke disease recurrence and to determine the factors that affect the case. Parameter Estimation in Stratified Cox models using Partial Maximum Likelihood Estimation (MPLE). Stratified Cox model building procedure consists of six stages: (1) identification data, which specify the variables that will be used in the Cox models. (2) Estimated Cox Proportional Hazard model parameters. (3) The test parameters for each variable using the Wald test. (4) Testing Proportional Hazard assumptions. (5) stratification variables. (6) Interpretation Stratified Cox models. This study uses data of patients who experienced a hemorrhagic stroke unspecified with 7 independent variables such as age, sex, blood pressure, blood sugar, triglycerides, cholesterol and replications. Based on the testing parameters obtained three variables that influence such as age, cholesterol levels and repeat. Furthermore, in assuming Proportional Hazard showed that replicates variable Proportional Hazard did not meet the assumptions that need to be stratified. Unspecified hemorrhagic stroke patients aged over 50 years admitted to 3.230 times longer than the patients were under 50 years old. Unspecified hemorrhagic stroke patients with high cholesterol levels are treated 0.182 times faster than patients with normal cholesterol levels. Keywords: Stratified Cox, Cox Proportional Hazard, MPLE, Haemorrhagic Stroke, Recurrent Events]]>
<![CDATA[PERAMALAN OUTFLOW UANG KARTAL DI BANK INDONESIA WILAYAH JAWA TENGAH DENGAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) ]]>
<![CDATA[Aukhal Maula Fina]]>;
<![CDATA[Tarno Tarno]]>;
<![CDATA[Rukun Santoso]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14691
<![CDATA[Generalized Space Time Autoregressive (GSTAR) model is a method that has interrelation between time and location or called with space time data. This model is generalization of Space Time Autoregressive (STAR) model where GSTAR more flexible for data with heterogeneous location characteristics. The purposes of this research are to get the best GSTAR model that will be used to forecast the outflow in the Bank Indonesia Office (BIO) Semarang, Solo, Purwokerto and Tegal. The best model obtained in this study is GSTAR (11) I(1) using the inverse distance weighting locations. This model has an average value of MAPE 35.732% and RMSE 440.52. The best model obtained explains that the outflow in BIO Semarang, Solo and Purwokerto are affected by two time lag before while for outflow in BIO Tegal is affected by two time lag befor and outflows in three other BIO. Keywords: GSTAR, Space Time, Outflow, Currency]]>
<![CDATA[PEMBENTUKAN POHON KLASIFIKASI BINER DENGAN ALGORITMA CART (CLASSIFICATION AND REGRESSION TREES) (Studi Kasus: Kredit Macet di PD. BPR-BKK Purwokerto Utara) ]]>
<![CDATA[Mardika, Zulfa Wahyu]]>;
<![CDATA[Mukid, Moch. Abdul]]>;
<![CDATA[Yasin, Hasbi]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i3.14715
<![CDATA[Modernization and globalization of the world today has entered into various lines of Indonesian society. One consequence is people's lifestyles are more consumptive. This lifestyle causes people take out a loan at a bank or other financial institution to fulfill his wish. Some people pay the loan on credit. But in implementation, there is a variety of things causes the credit not running properly or called with problem loan. As a service provider of credit institutions, PD. BPR-BKK Purwokerto Utara is also not free from this problem. Therefore, it is necessary to classify customers based on demographic variables using Classification and Regression Trees (CART) to minimize the chances of problem loans. Based on analysis of customer credit status data PD. BPR-BKK Purwokerto Utara, optimal classification tree formed by the number of terminal nodes as much as 6 nodes. This means there are 6 characteristics of customers PD. BPR-BKK Purwokerto Utara. And level of accuracy of the classification tree in classifying credit status of customers is 81.0 % . Keywords: Modernization, Globalization, Credit, Problem Loan, Customer, CART, Classification Tree.]]>
