Triastuti Wuryandari
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ANALISIS DESAIN FAKTORIAL FRAKSIONAL 2k-p DENGAN METODE LENTH Gian Kusuma Diah Tantri; Tatik Widiharih; Triastuti Wuryandari
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (713.049 KB) | DOI: 10.14710/j.gauss.v4i3.9432

Abstract

Rancangan faktorial fraksional banyak digunakan dalam percobaan terutama di bidang industri karena dapat menentukan pengaruh faktor utama dan interaksi terhadap respon. Rancangan yang melibatkan k buah faktor dengan dua taraf dan menggunakan 2-p fraksi dari percobaan faktorial lengkap disebut rancangan faktorial fraksional 2k-p. Penentuan faktor signifikan jika data yang diamati tanpa pengulangan dapat diuji dengan menggunakan metode Lenth. Penelitian ini bertujuan untuk menentukan penaksir dan statistik uji untuk mendapatkan faktor signifikan dengan metode Lenth, serta menentukan perbedaan dalam penggunaan metode Lenth dengan metode klasik. Kasus yang digunakan adalah rancangan faktorial fraksional 25-1 dengan faktor A, B, C, D, E. Hasil pengujian dengan metode Lenth diperoleh nilai estimasi S0 dan  sebagai penaksir awal dan akhir. Nilai Margin Error dan Simultan Margin Error sebagai batas kesalahan dalam penentuan faktor signifikan. Faktor yang berpengaruh terhadap respon adalah faktor B dan C. Apabila diuji dengan metode klasik diperoleh faktor yang berpengaruh terhadap respon adalah faktor B, C, D, E, AB, AC, dan BC, sehingga dapat dikatakan bahwa metode klasik lebih sensitif daripada metode Lenth. Kata kunci: Faktorial, fraksional, tanpa pengulangan, plot probabilitas normal, metode Lenth
IDENTIFIKASI CURAH HUJAN EKSTREM DI KOTA SEMARANG MENGGUNAKAN ESTIMASI PARAMETER MOMEN PROBABILITAS TERBOBOTI PADA NILAI EKSTREM TERAMPAT (Studi Kasus Data Curah Hujan Dasarian Kota Semarang Tahun 1990-2013) Annisa Rahmawati; Agus Rusgiyono; Triastuti Wuryandari
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.692 KB) | DOI: 10.14710/j.gauss.v3i4.8067

Abstract

The methods used to analyze extreme rainfall is the Extreme Value Theory (EVT). One of the approaches of EVT is the Block Maxima (BM) which follows the distribution of Generalized Extreme Value (GEV). In this study, the dasarian rainfall data of 1990-2013 in the Semarang City is divided based on block monthly and the month examined are October, November, December, January, February, March and April. The resulted blocks are 24 with 3 observations each block. Estimated parameter of form, location and scale are obtained by using the method of Probability Weight Moments (PWM). The result of this study is January has the greatest occurrence chance of extreme value with the value of estimated parameter of form 0,3840564, location 138,8152989 and scale 68,6067117. In addition, the alleged maximum value of dasarian rainfall obtained in a period of 2, 3, 4, 5 and 6 years are 243,45753 mm, 308,23559 mm, 357,26996 mm, 397,96557 mm and 433,28889 mm. Keywords: rainfall, Extreme Value Theory, Block Maxima, Generalized Extreme Value, Probability Weight Moments
PENGARUH MARKETING MIX TERHADAP KEPUASAN DAN LOYALITAS KONSUMEN MENGGUNAKAN METODE STRUCTURAL EQUATION MODELLING (SEM) Syarah Widyaningtyas; Triastuti Wuryandari; Moch. Abdul Mukid
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (737.852 KB) | DOI: 10.14710/j.gauss.v5i3.14712

Abstract

Marketing mix is a combination of variables that constitute the core of marketing system, consisting a set of variables that can be controlled and used by companies to influence consumer responses in target markets comprise. One that used in this study for analysis is Structural Equation Model (SEM). The study shows that satisfaction influenced by promotion, pricing, product and location of 38,9%, that loyalty is explained by satisfaction, promotion, pricing, product and location of 99,8%. In significant testing, it was found that pricing, product, location are significant to satisfaction. Satisfaction is significant to loyalty; while pricing, location, product are not significant to loyalty. Promotion is not significant to satisfaction and loyalty. Based on the results of data processing using software AMOS 22.0, the model SEM has been convenient and fit for use in research because the data has been proven to have normal distribution and have met the criteria for Goodness of Fit.Keywords: Marketing Mix, Consumer Satisfaction, Consumer Loyalty, Structural Equational Modelling.
PENDEKATAN REGRESI POLINOMIAL ORTHOGONAL UNTUK MENENTUKAN KADAR SALINITAS DAN KONSENTRASI LARUTAN KITOSAN PADA PEMBUATAN ANTIBAKTERI Haryanti Novitasari; Triastuti Wuryandari; Sugito Sugito
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.73 KB) | DOI: 10.14710/j.gauss.v3i3.6452

Abstract

Indonesia is one of the countries with big marine resource. It can cause increased marine waste, such as the shells. Shells can be processed into chitosan. Chitosan has the benefits with high economic value, one of the benefit is became a source of natural antibacterial. Antibacterial test of the chitosan and salinity of the S. aureus bactery indicating inhibition zone formation. The larger inhibition zone indicated that antibacterial produced  is better. To optimize the level of salinity and concentration of chitosan so this is used polynomial orthogonal regression approach. This approach can be done on design with the quantitative factors and it have same distance. Determination of the degree of polynomial orthogonal based on orthogonal contrasts that have significant factor of salinity and concentration of chitosan, then it can be determined the shape of regression equation. From the that equation can be determined the extreme points using a differential count. When return to the form of the design it can be determined in what  levels of salinity and concentration of chitosan that can maximize the inhibition zone in millimeters. After optimization obtained maximum value of salinity is 18,2846375915% and concentration of chitosan is 1,999699328% with assessment of inhibition zone of antibacterial for S. aureus is 1,72486650 mm. 
OPTIMALISASI PROSES PRODUKSI YANG MELIBATKAN BEBERAPA FAKTOR DENGAN LEVEL YANG BERBEDA MENGGUNAKAN METODE TAGUCHI Annisa Intan Mayasari; Triastuti Wuryandari; Abdul Hoyyi
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.488 KB) | DOI: 10.14710/j.gauss.v3i3.6440

Abstract

Taguchi method is a method that purposes to improve the quality of products and processes at the same time with the purpose of reducing costs and resources to a minimum. Taguchi method is one example of a fractional factorial design that uses orthogonal arrays to reduce the number of experiments. The analytical tool used was ANOVA and Signal to Noise Ratio. ANOVA was used to determine the factors that affect the response and Signal to Noise Ratio are used to determine the combination of factors that affect the performance of the product so that the resulting optimal response. Based on the results of tests performed to determine the factors that influence the design of electronic circuits that will produce the center frequency of 35.75 megahertz at a temperature of -10 ℃, the significant factor is the factor A, B, C, D, F, and H. The best combination is obtained A2, B2, C2, D3, F2, dan H3. Factor F has the greatest percent contribution is 42.57%, the next factor D, H, C, A, and B, respectively 8.83%, 7.37%, 5.93%, 3.90% and 3.84%.
KAJIAN AVAILABILITAS PADA SISTEM KOMPONEN SERI Avida Nugraheni C.; Sudarno Sudarno; Triastuti Wuryandari
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (424.788 KB) | DOI: 10.14710/j.gauss.v2i3.3664

Abstract

Availability is a measure of system performance and measures the combined effect of reliability, maintenance and logistic support on the operational effectiveness of the system. Availability of series system is derived from inherent availability of system that takes effect from mean time to failure (MTTF) and mean time to repair (MTTR). Given observed time data of microcontroller consists of processor core, memory and programmable I/O peripheral in series, is measured its system availability. By simple linier regression method, the parameter estimation is determined after data distribution known, for the mean time. Processor core has Weibull distribution for failure time data with ,   and  as regression model while repair time data is lognormal distribution with ,  and regression model is . Memory has exponential failure time data with  and  as regression model while normal repair time data has  dan  and regression model is . Failure time data distribution of programmable I/O peripherals is Weibull with ,   and regression model  while lognormal repair time data has ,  and regression model is . Due to MTTF is 11364.57 hours and MTTR is 41.59 hours, processor core’s availability is 99.64%. Availability of memory is 99.87% from MTTF is 20000 hours and MTTR is 27 hours. Programmable I/O peripheral has 18773.41 hours as MTTF and MTTR is 38.67 hours that deliver availability 99.79%. The series system availability is 99.30% means the probability of system is in the state of functioning at given time is 99.30%.
ANALISIS VARIAN DUA FAKTOR DALAM RANCANGAN PENGAMATAN BERULANG ( REPEATED MEASURES ) Alif Hartati; Triastuti Wuryandari; Yuciana Wilandari
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (486.267 KB) | DOI: 10.14710/j.gauss.v2i4.3779

Abstract

The experimental design is a series of tests, both using descriptive statistics and inferential statistics that aims to transform the input variables into an output which is the response of the experiment. In one study, the response sometimes observed in every experiment performed more than once at different times during the study called with Repeated Measures. Observation time as if viewed as an additional factor, resulting in a repeated measures seen as a two-factor design with split-plot patterns. Factors that attempted allocated as main plots and allocated observation time as a subplot. Step-by-step analysis to test the normality of the error, test the homogeneity of variance, determine the degrees of freedom, sum of squares and mean squares of each factor. The next hypothesis to test for factor a, factor b and interaction affect both whether the observed response. If any effect, it is necessary to further test the Duncan test. The data used are secondary data on the effect of temperature, time of observation and interaction both the amylase enzyme produced by the bacterium bacillus subtilis. Results obtained by the analysis of temperature, time of observation and interaction both significantly influence the observed response.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMAAN PESERTA DIDIK SMA NEGERI 2 SEMARANG MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL Galuh Riani Putri; Yuciana Wilandari; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (735.871 KB) | DOI: 10.14710/j.gauss.v5i3.14696

Abstract

Education can be used to determine the standard quality of life. One way to get an education is studying in the schools. In Semarang, there are several schools, one of which is SMAN 2 Semarang. In order to pass the admission selection of students at SMAN 2 Semarang, students must fulfill the requirements that had specified by the school. To determine the factors that affect the acceptance of students, the author uses ordinal logistic regression method. Ordinal logistic regression method is used to model the relationship between the response variable that consists of more than two categories and there are levels in that category with several independent variables that are categories or continuous. After doing research using ordinal logistic regression method, the result is that the factors that affect the acceptance of students of SMAN 2 Semarang is Indonesian scores, English scores, Mathematics scores, Science scores, Benefit scores, Achievement scores and also Rayon with the accuracy of the classification by 89, 63%. Keywords: Education, Admission of Students, Ordinal Logistic Regression
PENDETEKSIAN INFLUENTIAL OBSERVATION PADA MODEL REGRESI LINIER MULTIVARIAT MENGGUNAKAN JARAK COOK TERGENERALISASI (STUDI KASUS INDIKATOR PENDIDIKAN PROVINSI JAWA TENGAH TAHUN 2010) Puti Cresti Ekacitta; Diah Safitri; Triastuti Wuryandari
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (570.668 KB) | DOI: 10.14710/j.gauss.v1i1.906

Abstract

Multivariate linear regression model is regression model with one or more response variable and one or more predictor variable, with each response variable are mutually. In multivariate linear regression model sometimes often found Influential Observation. Influential Observation give most contributing in estimating regression coefficient. For detection Influential Observation on multivariate linear regression model is used Generalized Cook’s Distance. The aim of this research is to detection any or not any Influential Observation on multivariate linear regression model of education indicator in Central Java Province with response variable are Gross Participation Rate (APK), School Participation Rate (APS), and Pure Participation Number (APM) and predictor variable is percentage of population aged 10 years and over who graduated from junior high school. Result from this research  can be explained that if the percentage of population aged 10 years and over who graduated from junior high school increase one percent, it will have an impact on increasing gross participation rate the junior high school is 1.7849 % , increasing school participation rate is 1.6275 % and   increasing pure participation number is 1.3712 %. Also, from this results were obtained two observations are included Influential observation. Elimination of the two observations are included Influential observation in the multivariate linear regression model of education indicators in Central Java, affects the regression coefficients change only and does not have a major impact on the closeness of the relationship between response variables and predictor variables in the multivariate.
APLIKASI REGRESI DATA PANEL UNTUK PEMODELAN TINGKAT PENGANGGURAN TERBUKA KABUPATEN/KOTA DI PROVINSI JAWA TENGAH Tyas Ayu Prasanti; Triastuti Wuryandari; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (459.327 KB) | DOI: 10.14710/j.gauss.v4i3.9549

Abstract

Open unemployment rate is the percentage of the labor force that is unemployed and actively seeking employment to the total labor force. Unemployment data is a combination of cross section data and time series data are commonly called panel data. This study aims to be modeling the open unemployment rate in Central Java province in 2008 to 2013 by using panel data regression. To estimate the panel data regression model, there are three approaches, the common effect model, fixed effect model and random effects model. Estimation of panel data regression model is used the fixed effect model with cross section weight. The model show that the percentage of population aged 15 years and over who worked by the highest education attained is Senior High School/Vocational School, Senior High School Gross Enrollment Rate (GER), dependency ratio and Gross Regional Domestic Product (GDP) significantly affect the open unemployment rate by generating  for 81,65 %. Keywords: Cross Section Weight, Fixed Effect Model, Panel Data Regression, Open Unemployment, Central Java Province