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[PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAN TAHUN 2008-2013 DENGAN MENGGUNAKAN REGRESI DATA PANEL ]]>
<![CDATA[Muhammad Rizki]]>;
<![CDATA[Agus Rusgiyono]]>;
<![CDATA[Moch. Abdul Mukid]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8582
<![CDATA[Human Development Index (HDI) is a way to measure the success of human development based on a number of basic components quality of life. HDI is formed by three basic variables namely health, education and decent living standards. This study aims to identify factors that influence the Human Development Index in Central Java Province and get a model Human Development Index in Central Java province in 2008-2013. The data used in this study is a combination of cross section data and time series data are commonly called panel data, then this HDI modeling using panel data regression. There are three estimation of panel data regression model namely Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). Estimation of panel data regression model used is the Fixed Effects Model (FEM). FEM estimation results show the number of health facilities, school participation rate and Labor Force Participation Rate significantly affect the HDI by generating for 93.58%.Keywords : Fixed Effect Model, panel data regression, HDI in Central Java Province]]>
<![CDATA[PERAMALAN HARGA SAHAM DENGAN METODE EXPONENTIAL SMOOTH TRANSITION AUTOREGRESSIVE (ESTAR) (Studi Kasus pada Harga Saham Mingguan PT United Tractors) ]]>
<![CDATA[Rahmayani, Dwi]]>;
<![CDATA[Ispriyanti, Dwi]]>;
<![CDATA[Mukid, Moch. Abdul]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8424
<![CDATA[The stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 % are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPEThe stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 % are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPE]]>
<![CDATA[PENENTUAN MODEL KEMISKINAN DI JAWA TENGAH DENGAN MULTIVARIATE GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) ]]>
<![CDATA[Sindy Saputri]]>;
<![CDATA[Dwi Ispriyanti]]>;
<![CDATA[Triastuti Wuryandari]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8400
<![CDATA[The problem of poverty is a fundamental problem faced in a number of regions in Indonesia, to determine significant indicators on poverty by taking into account the spatial variation in the province of Central Java can use multivariate models Geographically Weighted Regression (MGWR). In the model MGWR model parameter estimation is obtained by using Weighted Least Square (WLS). Selection of the optimum bandwidth using Cross Validation (CV). The study looked for the best model among MGWR with multivariate regression and create distribution maps counties and cities in the province of Central Java based variables significantly to poverty. The results of testing the suitability of the model shows that there is no influence of spatial factors on the percentage of poor and non-poor in the province of Central Java. Variables expected to affect the percentage of poor people is a variable percentage of expenditures for food, while the percentage of the non-poor is a variable percentage of expenditure on food and the percentage of heads of household education level less than SD. Based on the AIC and the MSE obtained the best model is the model MGWR with AIC value of 44.4603 and MSE 0.454.Keywords: Cross Validation, MGWR, Poverty, Weighted Least Square]]>
<![CDATA[PENGELO MPOKAN KUALITAS UDARA AMBIEN MENURUT KABUPATEN/KOTA DI JAWA TENGAH MENGGUNAKAN ANALISIS KLASTER ]]>
<![CDATA[Rizki Taher Dwi Kurniawati]]>;
<![CDATA[Rita Rahmawati]]>;
<![CDATA[Yuciana Wilandari]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8588
<![CDATA[Ambient air is free of air inhaled daily by living creatures. Ambient air quality can be said to be decreased which is known from the results of measuring the quality of ambient air. The measurements carried out on residential areas, industrial areas, and traffic congested area and to SO2, NO2, CO, and HC. To help find solutions used cluster analysis of air pollution. Cluster analysis classifying objects based on object similarity. Similarities object seen by the small size of the Euclidean distance. The process of clustering with average linkage method performed on the data type of the region and type of pollutants. Clustering process produces two clusters for different kinds of land and 2 clusters for these types of pollutants. From the analysis on the type of region, cluster 1 is composed of 33 districts/cities with the results of measuring between 507 to 6760 can be said to have a good air quality conditions and in cluster 2 consists of two districts/cities with the results of measuring 11856.6 and 10594.8 is said to have poor air quality conditions. On the type of pollutant, Cluster 1 consists of 34 districts/cities with the measuring between 30 to 10810 which is said to have good air condition and the second cluster consists of one district/cities that have poor air conditions with a value of 20095 HC pollutants Keywords: ambient air, euclidean, average linkage, cluster analysis.]]>
<![CDATA[PENDEKATAN SERVQUAL-LEAN SIX SIGMA MENGGUNAKAN DIAGRAM KONTROL T2 HOTELLING UNTUK MENINGKATKAN KUALITAS PELAYANAN PENDIDIKAN (Studi Kasus di Jurusan Statistika Universitas Diponegoro) ]]>
<![CDATA[Darwati, Lulus]]>;
<![CDATA[Mustafid, Mustafid]]>;
<![CDATA[Suparti, Suparti]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8578
<![CDATA[Measurement the service of quality has an important role in improving and evaluating the performance of a service process. Measuring the service of quality is not as easy as measuring the goods quality, because the assessment service is subjective. Therefore, ServQual dimension is used as a tool to measure the performance of service from the perspective of service’s users. Lean Six Sigma method is used to improve the performance of the services of quality that focused on the reduction of variations and the increasing of the speed of the process through the elimination of waste that occur in the flowing process. This research aims to implement the integration of ServQual and Lean Six Sigma method by controlling the process using Hotelling T2 control charts on the improvement of the quality of education services. The performance of the education services process overall is indicated by the value of the capabilities and the level of the sigma. The capability value amount 0.8407 and the level of sigma amount 2.748 indicates that the waste percentage in the process of educational services is about 10.6%. The waste of dominant on improving the quality of education services such as lecturer competencies, the status of departement accreditation, the speed in the administrative services, and the refinement of laboratory facilities especially the improvement on the computer facilities.Keywords : ServQual, Hotelling T2 control charts, Process Capability, Lean Six Sigma]]>
<![CDATA[PERBANDINGAN ANALISIS KLASIFIKASI NASABAH KREDIT MENGGUNAKAN REGRESI LOGISTIK BINER DAN CART (CLASSIFICATION AND REGRESSION TREES) ]]>
<![CDATA[Agung Waluyo]]>;
<![CDATA[Moch. Abdul Mukid]]>;
<![CDATA[Triastuti Wuryandari]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8420
<![CDATA[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]]>
<![CDATA[VALUASI COMPOUND OPTION PUT ON CALL TIPE EROPA PADA DATA SAHAM FACEBOOK ]]>
<![CDATA[Muhammad Sunu Widianugraha]]>;
<![CDATA[Di Asih I Maruddani]]>;
<![CDATA[Diah Safitri]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8583
<![CDATA[Option is a contract that gives the right to individuals to buy (call options) or sell (put options) the underlying asset by a certain price for a certain date. One type of options that are traded is compound options. Compound option model is introduced by Robert Geske in 1979. Compound option is option on option. Compound option put on a call is put option where the underlying asset are call option. An empirical study using compound option put on a call stocks of Facebook. It has strike price compound option US$ 77.5 and strike price call option US$ 80, with the first exercise date on September 26, 2014 and the second exercise date on October 31, 2014. The theoritical price of compound option put on call on stocks of Facebook is US$ 75.65048. Keywords: Compound option, put on a call, option stocks of Facebook, Black-Scholes model, theoritical price.]]>
<![CDATA[PEMILIHAN PENGRAJIN TERBAIK MENGGUNAKAN MULTI-ATTRIBUTE DECISION MAKING (MADM) TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS) (STUDI KASUS : PT. Sinjaraga Santika Sport, Majalengka) ]]>
<![CDATA[Fizry Listiyani Maulida]]>;
<![CDATA[Tatik Widiharih]]>;
<![CDATA[Alan Prahutama]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8574
<![CDATA[The human resources (HR) known as the employess are the successful of the company. PT. Sinjaraga Santika Sport (Triple’S) is a handmade football company by the craftsmen. Most of the craftsmen go to the rice fields on the growing season or the harvest season. So selection of the best craftsmen is needed in order to the production of the football don’t have problems. The selection uses TOPSIS method. TOPSIS is one of method that can be used to solve MADM problem. The steps of TOPSIS method are calculated the normalized decision matrix, determined the weight, calculated the weighted normalized decision matrix, determined the positif-ideal solutions and negatif-ideal solutions, calculated the separation measures, and calculated the preference value. There are 25 craftsmen and six criteria. The criteria are neatness of the ball, accurateness stitching of the ball, number of the ball, accurateness logo of the ball, cleanness of the ball, and defect proportion. The results in this reseach are the best carftsmen has 0,78861 of preference value and the worst craftsmen has 0,16642 of preference value. Preference value by manual calculate equal with preference value by GUI Matlab. Keywords : TOPSIS, MADM, carftsmen]]>
<![CDATA[PEMODELAN PERSENTASE BALITA GIZI BURUK DI JAWA TENGAH DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION PRINCIPAL COMPONENTS ANALYSIS (GWRPCA) ]]>
<![CDATA[Novika Pratnyaningrum]]>;
<![CDATA[Hasbi Yasin]]>;
<![CDATA[Abdul Hoyyi]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8401
<![CDATA[Geographically Weighted Regression Principal Components Analysis (GWRPCA) is a combination of method of Principal Components Analysis (PCA) and Geographically Weighted Regression (GWR). PCA is used to eliminate the multicollinearity effect in regression analysis. GWR is a local form of regression and a statistical method used to analyze the spatial data. In GWRPCA predictor variables is a principal components of the PCA result. Estimates of parameters of the GWRPCA model can use Weighted Least Square (WLS). Selection of the optimum bandwidth use Cross Validation (CV) method. Conformance testing PCA regression and GWRPCA models approximated by the F distribution, while the partial identification of the model parameters using the t distribution. In PCA obtained variables that affect the percentage of severe children malnutrition in Central Java in 2012 can be represented or replaced with PC1 and PC2 which can explain the total variance of data is 78.43%. Application GWRPCA models at the percentage of severe children malnutrition in Central Java in 2012 showed every regency locations have different model with global coefficient of determination is 0.6313309 and the largest local coefficient of determination is 0.72793026 present in Batang regency, while the smallest local coefficient of determination is 0.03519539 present in Sukoharjo regency. Keywords : Severe Malnutrition, Multicollinearity, Geographically Weighted Regression Principal Components Analysis, Weighted Least Square,Coefficient of Determination.]]>
<![CDATA[PERHITUNGAN SUKU BUNGA EFEKTIF UNTUK PENENTUAN ALTERNATIF PEMBIAYAAN KENDARAAN MOTOR PADA LEASING DAN BANK DENGAN METODE INTERPOLASI LINIER (Studi Kasus Harga Sepeda Motor Honda Beat Injeksi Terdaftar Bulan September 2014) ]]>
<![CDATA[Swasnita Swasnita]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Sugito Sugito]]>
<![CDATA[ Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v4i2.8589
<![CDATA[Imposition of interest rates by the bank and leasing in providing credit is different. The interest rate usually not included in the brochure loan installments. The calculation of the interest rate can be calculated using the flat rate and the effective interest rate. In the calculation of the effective interest rate can be performed using linear interpolation. Determination of the motorcycle financing alternative most favorable to the customer, can be seen from the lowest interest rates charged. The results of the case study Honda Beat injection prices listed September 2014 on credit motorcycle through leasing Central Sentosa Finance (CSF), leasing Adira Multifinance (Adira) and credit through Bank Rakyat Indonesia showed the lowest interest rate on the lease Central Sentosa Finance (CSF). In addition to low interest rates charged are other benefits that the filing procedures quickly and without collateral (guarantee). Keywords : Flat Interest Rate, Effective Interest Rate, Linear Interpolation, Leasing, Bank]]>
