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[PENENTUAN MODEL RETURN HARGA SAHAM DENGAN MULTI LAYER FEED FORWARD NEURAL NETWORK MENGGUNAKAN ALGORITMA RESILENT BACKPROPAGATION (Studi Kasus : Harga Penutupan Saham Unilever Indonesia Tbk. Periode September 2007 – Maret 2015) ]]>
<![CDATA[Riza Adi Priantoro]]>;
<![CDATA[Dwi Ispriyanti]]>;
<![CDATA[Moch. Abdul Mukid]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.11058
<![CDATA[Determination of a return of stock price model is often associated with a process of forecasting for future periods. A method that can be used is neural network. The use of neural network in the field of forecasting can be a good solution, but the problem is how to determine the network architecture and the selection of appropriate training methods. One possible option is to use resilent back propagation algorithm. Resilent back propagation algorithm is a supervised learning algorithm to change the weights of the layers. This algorithm uses the error in the backward direction (back propagation), but previously performed advanced stage (feed forward) to get the error. This algorithm can be used as a learning method in training model of a multi-layer feed forward neural network. From the results of the training and testing on the share return of stock price PT. Unilever Indonesia Tbk. data obtained MSE value of 0.0329. This model is good to use because it provides a fairly accurate prediction of the results shown by the proximity of the target with the output.Keywords : return, neural network, back propagation, feed forward, back propagation algorithm, weight, forecasting.]]>
<![CDATA[PENERAPAN METODE ORDINARY KRIGING PADA PENDUGAAN KADAR NO2 DI UDARA (Studi Kasus: Pencemaran Udara di Kota Semarang) ]]>
<![CDATA[Gera Rozalia]]>;
<![CDATA[Hasbi Yasin]]>;
<![CDATA[Dwi Ispriyanti]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.11034
<![CDATA[Air pollution must be addressed. Nitrogen Dioxide is one of the important factors in air pollution. To determine concentration level of the pollutant “Badan Lingkungan Hidup Kota Semarang” already take measurements at several points. However, because of blocked considerable cost, is not much point to do measurements. In this study, will be used Ordinary Kriging method to estimate at some points in Semarang. In this methode will compare the value of the eksperimental semivariogram with some theoretical semivariogram models (spherical, eksponensial, and gaussian) to get the best model that will be used in the estimation. In this study, estimate the concentration of Nitrogen Dioxide in the air in a number of village in Semarang. Based on analysis we found the best model is spherical model with Nitrogen Dioxide produces estimates is the highest in Sub Gebangsari and Nitrogen Dioxide lowest in Sub Patemon. Keywords: Ordinary Kriging, Semivariogram, Nitrogen Dioxide.]]>
<![CDATA[PEMODELAN REGRESI 3-LEVEL DENGAN METODE ITERATIVE GENERALIZED LEAST SQUARE (IGLS) (Studi Kasus: Lamanya pendidikan Anak di Kabupaten Semarang) ]]>
<![CDATA[Amanda Devi Paramitha]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Triastuti Wuryandari]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.10909
<![CDATA[In a research, data was used often hierarchical structure. Hierarchical data is data obtained through multistage sampling from a population with independent variables can be defined within each level and dependent variable can be defined at the lowest level. One analysis that can be used for data with a hierarchical structure is a multilevel regression analysis. The purpose of this final three-level regression analyzes to establish regression models about the length of a child's education in the District of Semarang where the individual level-1 with a factor of gender, lodged at the family level-2 by a factor of the length of father's education and duration of maternal education and nesting on the environment level-3 with factor of residence, number of elementary school the large number of junior high school and the large number of high school. Parameter estimation in 3-level regression models can use several methods, one of which is a method of Iterative Generalized Least Square (IGLS). Of cases the length of education in the district of Semarang indicate that factors influencing factor is the length of father's education and the duration of the mother's education. Keywords : Hierarchical structure, multistage sampling, multilevel regression, Iterative Generalized Least Square.]]>
<![CDATA[PENDEKATAN METODE SIX SIGMA-TAGUCHI DALAM MENINGKATKAN KUALITAS PRODUK (Studi Kasus PT. Asaputex Jaya Spinning Mill Tegal) ]]>
<![CDATA[Nesvi Intan Oktajayanti]]>;
<![CDATA[Mustafid Mustafid]]>;
<![CDATA[Sudarno Sudarno]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.11039
<![CDATA[The main factors to achieve business success in the era of globalization is the quality. In the business world, quality control is the key to maintaining customer loyalty. For a company doing quality control is expected to achieve the company's goals, related to the company's revenue. This is the purpose of PT. Asaputex Jaya Spinning Mill Tegal to make efforts to improve the production activities, especially in improving quality by reducing defects. Six Sigma-Taguchi method can be used to improve quality yarns product. From the analysis we found that the control diagram p, data of defects is uncontrolled, so the capability process is still low with capability value is 0,502. So, it need to be improved to enhance product quality yarns. By using the Taguchi method we can know factors and optimal level to improve the quality of the yarn. That Factor and level is TPI with the optimum level that can be used are Level 2 (13,5 rpm), level 1 (383 tpm) for Delivery Speed factor, for the weight of cotton the optimum level is level 1 (2,0 Kw) and factor Grain the optimal level is level 2 (400 Ne). Keywords: Six Sigma Method, Design Experiment of Taguchi, Capability Process]]>
<![CDATA[PENDUGAAN AREA KECIL TERHADAP PENGELUARAN PER KAPITA DI KABUPATEN SRAGEN DENGAN PENDEKATAN KERNEL ]]>
<![CDATA[Bitoria Rosa Niashinta]]>;
<![CDATA[Dwi Ispriyanti]]>;
<![CDATA[Abdul Hoyyi]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.10936
<![CDATA[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]]>
<![CDATA[ANALISIS ANTRIAN PASIEN INSTALASI RAWAT JALAN POLIKLINIK LANTAI 1 DAN 2 RSUD CENGKARENG, JAKARTA ]]>
<![CDATA[Nadeak, Sanitoria]]>;
<![CDATA[Sugito, Sugito]]>;
<![CDATA[Suparti, Suparti]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.11059
<![CDATA[The queue process associates with the arrival of the costumers of a service facility, waiting in a queue line when all waiters are busy, and finally left the facility after being served. Queuing phenomena can be found in public service facilities, such as in District General Hospital (RSUD) Cengkareng. The length of the registration procedure, consultation services for physicians, and waiting time for the pharmacy services, can influence the satisfaction of the patients of Outpatient Installation of RSUD Cengkareng. Therefore, it is necessary to have an appropriate queue model to get an effective service, balanced and efficient, that can reduce the long queues and waiting time. From the analysis, the queue model for the registration of the Workers Social Security Agency (BPJS) patient is (M /M/6):(GD/∞/∞) with the number of server is 6 counters and for the non BPJS patients is (M/M/2):(GD/∞/∞) with the number of server is 2 counters. The queue model for the psychiatrist clinic and anesthetic is (M/M/1):(GD/∞/∞) with the number of server is 1 counter. The queue model for the other Polyclinic is (M/M/c):(GD/∞/∞) with the number of server depends on the clinic itself.Keywords: Queue, Outpatient Installation, District General Hospital (RSUD) Cengkareng]]>
<![CDATA[PEMODELAN UPAH MINIMUM KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN FAKTOR-FAKTOR YANG MEMPENGARUHINYA MENGGUNAKAN REGRESI RIDGE ]]>
<![CDATA[Hildawati Hildawati]]>;
<![CDATA[Agus Rusgiyono]]>;
<![CDATA[Sudarno Sudarno]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.11035
<![CDATA[The least squares method is a regression parameter estimation method for simple linear regression and multiple linear regression. This method produces no bias and variance estimator minimum if no multicollinearity. But if it happens, it will generate a large variance and covariance. One way to overcome this problem is by using ridge regression. Ridge regression is a modification of the least squares by adding a bias constant on the main diagonal Z'Z. So that estimation parameter with . This method produces bias and variance estimator minimum. Results of the modeling discussion of minimum wage in the city of Semarang, Surakarta, Tegal and Banyumas as well as the factors that influence it, such as inflation, Gross Domestic Regional Product (DGRP) and the Desent Living Needs contained multicollinearity problem. The minimum wage is significantly influenced Semarang Desent Living Needs, while Surakarta and Banyumas significantly affected GDRP and Desent Living Needs. Keywords: multicollinearity, ridge regression, bias constants , the minimum wage]]>
<![CDATA[PENERAPAN DIAGRAM KONTROL MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING AVERAGE (MEWMA) PADA PENGENDALIAN KARAKTERISTIK KUALITAS AIR (Studi Kasus: Instalasi Pengolahan Air III PDAM Tirta Moedal Kota Semarang) ]]>
<![CDATA[Anastasia Arinda]]>;
<![CDATA[Mustafid Mustafid]]>;
<![CDATA[Moch. Abdul Mukid]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.10910
<![CDATA[Water treatment is intended to change the original water quality that does not fulfill the health requirements become a water for human consumption and must comply with the levels of certain parameters. Quality control can be done by forming a Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. In the Multivariate Exponentially Weighted Moving Average (MEWMA) control charts with λ = 0.25 and UCL = 13.92658 seen that process controlled statistically. Once the process is under control, it can be done analysis of the ability of the process to determine whether the process fulfill the specifications or not. In the calculation process capability univariate each characteristics and multivariate process capability index values obtained more than 1 means that the process is going well. Keywords: water quality, Multivariate Exponentially Weighted Moving Average (MEWMA), process capability.]]>
<![CDATA[PEMODELAN INDEKS PEMBANGUNAN MANUSIA MENGGUNAKAN SPATIAL PANEL FIXED EFFECT (Studi Kasus: Indeks Pembangunan Manusia Propinsi Jawa Tengah 2008 - 2013) ]]>
<![CDATA[Novian Trianggara]]>;
<![CDATA[Rita Rahmawati]]>;
<![CDATA[Hasbi Yasin]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.11040
<![CDATA[The success of a country could be seen from the condition of it society. A country needs to have developed society, a way to establish it is by human development. Human development is formed by three basic components, they are long and healthy life, knowledge, and decent living. Some indicators that represent these three components are summarized in one single value, the Human Development Index. This study models the Human Development Index for each city in Central Java using econometric approach by considering the specific spatial effect. The independent variable used were health facilities representing health component, School Participation Rate that represents education component, and Poverty Percentage that represents component of decent living standard. By using Spatial Panel Fixed Effect the best model is Spatial Autoregressive Model (SAR) with the influencing independent variabels are school participation rate and poverty percentage, with R2 of 99.54%.Keyword: HDI, Spatial, Panel, Fixed Effect]]>
<![CDATA[ANALISIS KLASIFIKASI NASABAH KREDIT MENGGUNAKAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) ]]>
<![CDATA[Desy Ratnaningrum]]>;
<![CDATA[Moch. Abdul Mukid]]>;
<![CDATA[Triastuti Wuryandari]]>
<![CDATA[ Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v5i1.11031
<![CDATA[Credit is one of the facilities provided by banks to lend money to someone or a business entity within the prescribed period. The smooth repayment of credit is essential for the bank because it influences the performance as well as its presence in daily life. Acceptance of prospective credit customers should be considered to minimize the occurrence of bad credit. Classification and Regression Trees (CART) is a statistical method that can be used to identify potency of credit customer status such as current credit and bad credit. The predictor variables used in this study are gender, age, marital status, number of children, occupation, income, tenor / period, and home ownership. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification and Regression Trees (Bagging CART) method. The classification of credit customers using Bagging CART gives the classification accuracy 81,44%. Key words : Credit, Bootstrap Aggregating Classification and Regression Trees (Bagging CART), Classification Accuracy]]>
