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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang

Triastuti Wuryandari

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Author-ID : 452063

Decision Sciences, Operations Research & Management Other

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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

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

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

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

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

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

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

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

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

PENENTUAN TREN ARAH PERGERAKAN HARGA SAHAM DENGAN MENGGUNAKAN MOVING AVERAGE CONVERGENCE DIVERGENCE (Studi Kasus Harga Saham pada 6 Anggota LQ 45) Tri Murda Agus Raditya; Tarno Tarno; Triastuti Wuryandari
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of many examples of technical indicator that frequently used for stock price analysis is Moving Average Convergence Divergence (MACD). MACD generates two signal called goldencross and deathcross are used to find the reversal momentum of stock price trend movement. Goldencross as a oversold point marker serves to give a buying signal. While, deathcross as a overbought point marker serves to give a selling signal. Research on six stocks member of LQ45 (ANTM, BWPT, MNCN, TINS, BJBR, and LPKR) during the period January 1 until October 31, 2012 managed to prove the accuracy of the signal formed by MACD signal. By applying the MACD Indicator consistently, investors can get a percentage of profit above the actual inflation rate in 2012 by Indonesian Bank. On these results, the goldencross and deathcross signal give a good performance as tool of technical analysis for determining the trend of the direction of stock price movements

ANALISIS INTERVENSI KENAIKAN HARGA BBM BERSUBSIDI PADA DATA INFLASI KOTA SEMARANG Novia Dian Ariyani; Triastuti Wuryandari; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Intervention model is a model of time series data analysis that originally used to explore impact of unexpectedly external events to the observation variable. In this study, an increases subsidized fuel price analysis has done in June 2013 (first step function) and November 2014 (second step function) for Semarang inflation data at January 2007 until January 2015 and purposed to obtain the intervention model and forecast the Semarang inflation for some time later. Based on the result of inflated subsidized fuel price analysis for Semarang inflation data, the resulted model is ARIMA (1,0,0) with first intervention order b = 1, s = 2, r = 0 and second intervention order b = 1, s = 1, r = 0. Furthermore, the model is used to forecast inflation in Semarang for forward some periods.Keywords: ARIMA, intervention analysis, step function, inflation, subsidized fuel.

Co-Authors Abdul Hoyyi Agus Rusgiyono Akhmad Zaki Alif Hartati Angga Saputra Desti Annisa Ayu Wulandari Annisa Intan Mayasari ANNISA RAHMAWATI Artha Ida Sri Anggriyani Arya Fendha Ibnu Shina Avida Nugraheni C. Ayu Hapsari Budi Utami Bellina Ayu Rinni Danardono Danardono Desriwendi Desriwendi Desy Rahmawati Ningrat Di Asih I Maruddani Diah Safitri Dian Ika Pratiwi Dwi Ispriyanti Fitriani Fitriani Friska Irnas Adiyani Galuh Riani Putri Gian Kusuma Diah Tantri Haryanti Novitasari Hasbi Yasin Ibnu Athoillah Indradewa, Rhian Ira Puspita Sari Ishlahul Kamal Landong Panahatan Hutahaean Lintang Ratri Wardhani Maralika Yundya Sari Moch. Abdul Mukid Mohamad Reza Pahlevi Mustafid Mustafid Nova Yanti Gultom Novia Dian Ariyani Oka Afranda Puti Cresti Ekacitta Rahma Kurnia Widyawati Restu Sri Rahayu Rita Rahmawati Rizky Ade Putranto Rosmalia Safitri Sarita Budiyani Purnamasari Sindy Saputri Solly Aryza Sudarno Sudarno Sugito Sugito Suparti Suparti Susi Ekawati Syarah Widyaningtyas Tarno Tarno Tatik Widiharih Tri Murda Agus Raditya Tyas Ayu Prasanti Vina Riyana Fitri Wulan Safitri Yuciana Wilandari Yuyun Naifular

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