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All Journal COPING (Community of Publishing in Nursing) Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Pendas : Jurnah Ilmiah Pendidikan Dasar Review of Accounting and Taxation Jurnal Riset Ilmu Kesehatan Umum dan Farmasi

Diah Safitri

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Author-ID : 2977048

Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Nursing Public Health Social Sciences Other

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DIAGRAM KONTROL MULTIVARIAT BERDASARKAN JARAK CHI-KUADRAT UNTUK QUALITY CONTROL PRODUKSI DI PT ARA SHOES Galuh Ayu Prameshti; Sudarno Sudarno; Diah Safitri
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Shoes are demands required by everyone. As a time changing and increasing demand for shoes, so many competitor shoe factories produce the best shoes for the customer. PT Ara Shoes is a famous shoe factory that has been well known for six decades. To be able to make fairness quality competition shoe factory would have to ability to produce a high quality product. To improve quality and production process is the way to determine whether quality of production is already achieve the minimum standard quality needed by applying the minimum standard quality control system. Control charts based on chi-square distance is a diagram of the control that can be used for multivariate data attributes. Production processes at PT ARA Shoes is divided into 3 stages of the shoe production process, including the process of cutting, process of sewing and assembling process. The cases study examined in this observation is the production process of cutting from January 2012 - October 2013 total applying 22 observations. Based on the research that has been done it is concluded that the production process is not enough controlled in cutting and improvement needed to be done twice, by eliminating observations 4th and 5th.Keywords : shoes charts control, chi-square distance, PT ARA Shoes

PENERAPAN DIAGRAM KONTROL D^2 MAHALANOBIS PADA PROSES PRODUKSI MINUMAN KEMASAN RETURNABLE GLASS BOTTLE (Studi Kasus di PT. Coca-cola Bottling Indonesia Central Java) Muhammad Abid Muhyidin; Diah Safitri; Rita Rahmawati
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Quality being one of the basic factors in choosing a product consumers. Therefore, an industry or a company should always maintain the quality of their products in order to get loyal customers and are able to survive in the competitive market. Coca-cola Bottling Indonesia Central Java Limited Compay is one of the manufacturing company engaged in the beverage packaging industry and always trying to improve the quality for customer satisfaction. Although it has been to improve the quality, there are still defective product because it does not meet the quality characteristics. Monitoring the result of production process aims to determine whether the process is stable or not. Mahalanobis control chartis one of the control charts that can be used to monitor the production mismatch that is multivariate attributes. By using Mahalanobis control chart, beverage production process of returnable glass bottle (RGB) in Coca-cola Bottling Indonesia Central Java Limited Compay based on the characteristics of disability shows that the results have not yet stable and controllable. This is due to Mahalanobis control chartphase II there are 5 observations of 75 observations or 6.66 % identified uncontrolled observations

KLASIFIKASI KELULUSAN MAHASISWA FAKULTAS SAINS DAN MATEMATIKA UNIVERSITAS DIPONEGORO MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) Rizal Yunianto Ghofar; Diah Safitri; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Education is a top priority for today's society. The quality of education can be seen from the learning achievement. There are so many factors that influence learning achievement in this regard graduation, therefore, necessary to identify the most influential factors that will be used to improve the quality of education. This study was conducted to obtain a model that is capable of classifying the data Faculty of Science and Mathematics Diponegoro University Semarang graduation using Multivariate Adaptive Regression Spline (MARS) method. MARS is a nonparametric regression method that can be used for data of high dimension. To get the best MARS models, made possible combinations Basis Function (BF), Maximum Interaction (MI), and Minimum Observation (MO) by trial and error. The best model is the model that is used in combination with BF = 28, MI = 2, MO = 1 because it has the smallest GCV value that is equal to 0,17781. There are three variables that contribute to the MARS model of the variable GPA, majors and gender. As for the variable organization, part time, entry point, and scholarships do not contribute to the model. Obtained misclassification of 20,50%. Press's Q test value indicates that statistically MARS method has been consistent in classifying the data FSM Diponegoro University Semarang graduation.

PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN ANGKA PARTISIPASI PENDIDIKAN JENJANG SMA/MA/PAKET C DENGAN FUZZY SUBTRACTIVE CLUSTERING Onny Kartika Hitasari; Diah Safitri; Suparti Suparti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Education is one aspect of nation building is very important to realize the human resource development and national character. Awareness of the importance of education can be seen through education enrollment rates. This study aims to classify the enrollment rates in the district / city Central Java. The data used is the Gross Enrollment Rate (GER), Net Enrollment Rate (NER) and School Enrollment Rate (SER) at the district / city in Central Java Province in 2013. The grouping method used in this study is Fuzzy Subtractive Clustering. The results showed that the best cluster grouping enrollment rates in Central Java Province which consists of 4 clusters with value of cluster variant is 0.00749 and radii between 0.35 to 0.50. Keywords: education participation rate, GER, NER, SER, Fuzzy Subtractive Clustering

ANALISIS REGRESI LINIER PIECEWISE DUA SEGMEN Syilfi Syilfi; Dwi Ispriyanti; Diah Safitri
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical method that is widely used in research. In general, the regression analysis is the study of the relationship of one or more independent variables with the dependent variable. In analyze the functional relationship between X as the independent variables and Y as the dependent variable, there may be a linear relationship is different for each interval X. If the regression of X on Y has a linear relationship on the certain of the interval of X, but also has a distinct linear relationship at another interval of X, so the use of piecewise linear regression is appropriate in this case. Piecewise linear regression is a method in regression analysis that divided the independent variable into several segments based on a particular value called the X-knots, and in each segment of the data contained linear regression model. X-knot is a value on the independent variable, where X is the current value of the X-knots, it will form a linear regression equation of the line that is different than the current value of X is under X-knots. Piecewise linear regression can be applied in many fields, one of them in the waters of the analysis regarding the influence of river discharge on the basis of the number of transport sediman. By comparison MSE simple linear regression and multiple linear piecewise two segments, the result that the two segments piecewise linear regression is a model that describes the influence of river discharge on the basis of the number of bedload transport

KLASIFIKASI KELOMPOK RUMAH TANGGA DI KABUPATEN BLORA MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) DAN FUZZY K-NEAREST NEIGHBOR (FK-NN) Yani Puspita Kristiani; Diah Safitri; Dwi Ispriyanti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Good classification method will result on less classification error. Classification method developed rapidly. Two of the existing classification methods are Multivariate Adaptive Regression Spline (MARS) and Fuzzy K-Nearest Neighbor (FK-NN). This research aims to compare the classification of poor household and prosperous household based on per capita income which has been converted according to the poverty line between MARS and FK-NN method. This research used secondary data in the form of result of National Economy and Social Survey (SUSENAS) in Blora subdistrict in 2014. The result of the classification was evaluated using APER. The best classification result using MARS method is by using the combination of BF= 76, MI= 3, MO= 1 because it will result on the smallest Generalized Cross Validation (GCV) and the APER is 10,119 %. The best classification result using FK-NN method is by using K=9 because it will result on the smallest error and the APER is 9,523 %. The APER calculation shows that the classification of household in Blora subdistrict using FK-NN method is better than using MARS method. Keywords: Classification, MARS, FK-NN, APER, SUSENAS, Blora

PEMISAHAN DESA/KELURAHAN DI KABUPATEN SEMARANG MENURUT STATUS DAERAH MENGGUNAKAN ANALISIS DISKRIMINAN KUADRATIK KLASIK DAN DISKRIMINAN KUADRATIK ROBUST Afianti Sonya Kurniasari; Diah Safitri; Sudarno Sudarno
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Semarang Regency is one of 29 counties and 6 towns in Central Java province. In the district there are rural areas and urban areas. Discriminant analysis is a technique related to the separation of objects into different groups that have been set previously, thus, discriminant analysis can be used to separate village in Semarang Regency into urban or rural groups. Linear discriminant analysis assumes that the covariance matrix of the two groups are equal, If the assumption of equality covariance matrix is denied, function of quadratic discriminant can be used for classification. Classical estimation for the sample mean vector and sample covariance matrix is very sensitive to the presence of outliers in the observations and the functioning of the separation can be non-robust. Estimators that can be used to cope with data containing outliers are the Minimum Covariance Determinant. Robust discriminant analysis is obtained by replacing the mean and covariance matrix using the classic MCD estimator. After analysis is performed, obtained result the data of 2011 Village Potential contains outlier, so that the robust quadratic discriminant analysis more appropriate because it can provide precision the results of separation 89,79% while classical quadratic discriminant analysis give exactness of 87,23%.

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.

ANALISIS INTERVENSI KENAIKAN HARGA BBM TERHADAP PERMINTAAN BBM BERSUBSIDI PADA SPBU SULTAN AGUNG SEMARANG JAWA TENGAH Fandi Ahmad; Rita Rahmawati; Diah Safitri
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Fuel consumption is always interesting to study, in addition to the use of which is used by all the community but also because of the critical role of fuel as an indicator to determine the price of other staples. Not surprisingly, changes in fuel prices polemical definitely interesting to study. In this subject specifically on the impact of the fuel price hike subsidized fuel demand. Changes in fuel price (hike) will have an impact on people's behavior in anticipation of the event. Most people will take the step to buy fuel in bulk prior to the date of determination of the increase in fuel prices, resulting in a surge in demand for fuel. Intervention model is a time series model that can be used to model and predict the data containing the intervention of external factors. In the intervention model, there are two functions, namely the step and pulse functions. Step function is a form of intervention that occurs within a long period of time while the pulse function is a form of intervention that occurs only within a certain time. Based on the analysis suggests that the impact of the use of gasoline and diesel at the pump Sultan Agung Semarang wear both pulse function because the impact was immediate and occur only in a short time Keywords: subsidized BBM, time series, intervention models, pulse function, step function

ANALISIS INTERVENSI FUNGSI STEP (Studi Kasus Pada Jumlah Pengiriman Benda Pos Ke Semarang Pada Tahun 2006 – 2011) Amelia Crystine; Abdul Hoyyi; Diah Safitri
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Data time series yang dipengaruhi oleh beberapa kejadian yang disebut intervensi akan mengakibatkan perubahan pola data pada satu waktu t. Analisis intervensi terdiri dari dua fungsi yaitu fungsi step dan fungsi pulse.Time series data that are influenced by several events called the intervention will lead to changes in the pattern of data at a t time. Analysis of intervention consists of two functions, that is the step function and pulse function. Intervention of step function represents an intervention that have long-term effects, whereas pulse function represents an intervention that takes place at a particular time. Step function intervention model was created based on the delay time of the intervention (b), the length of the intervention effect (s), and the pattern of intervention effects that was occured after b + s period (r). Intervention modeling was done after ARIMA (Autoregressive Integrated Moving Average) model was acquired. ARIMA model was used to determine the b, s, and r order of intervention. In this study, the step function intervention analysis was used to assess the amount of postage on the period January 2006 to February 2011. Based on the analysis, the ARIMA model produced was ARIMA (0,1,1). Based on intervention response obtained residual value b = 4, s = 0, r = 2 is used to form a model of intervention using the least squares method.

Co-Authors Abdul Hoyyi Afianti Sonya Kurniasari Agus Rusgiyono Alan Prahutama Amelia Crystine Amir, Yufitriana Annisa Pratiwi Artha Ida Sri Anggriyani Aryono Rahmad Hakim Aulia Putri Andana Bisri Merluarini Budi Warsito Desy Trishardiyanti Adiningtyas Dhinda Amalia Timur Di Asih I Maruddani Dimas Kevin Natanael Dwi Ispriyanti Erin Padilla Siregar Erna Musri Arlita FANDI AHMAD Galuh Ayu Prameshti Hasbi Yasin Indri Puspitasari Lailly Rahmatika Mekar Sekar Sari Mertika Moch. Abdul Mukid Muhammad Abid Muhyidin Muhammad Sunu Widianugraha Mulyasari, Windu Nana Nofianti, Nana Ndaru Dian Darmawanti Nindy Citroresmi Nur Musrifah Rohmaningsih Nuril Faiz Onny Kartika Hitasari Puti Cresti Ekacitta Rahma Nurfiani Pradita Revaldo Mario Ridha Ramandhani Rita Rachmawati Rita Rahmawati Rizal Yunianto Ghofar Rizka Asri Brilliani Sherly Candraningtyas Sisca Agustin Diani Budiman Sudarno Sudarno Sugito Sugito Suparti Suparti Syilfi Syilfi Trianita Resmawati Triastuti Wuryandari Vierga Dea Margaretha Sinaga Vina Riyana Fitri Wella Rumaenda Yani Puspita Kristiani Yogi Setiyo Pamuji Yuciana Wilandari Yulia Irvani Dewi

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