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ISSN : - EISSN : 24770647 DOI : -

Core Subject : Science,

Decision Sciences, Operations Research & Management

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Articles 271 Documents

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PENGELOMPOKAN DAERAH PENGHASIL BAHAN DASAR TEPUNG KOMPOSIT DI INDONESIA MENGGUNAKAN METODE LATENT CLASS CLUSTER ANALYSIS (LCCA) Budiati, Shinta; Susanto, Irwan; Wibowo, Supriyadi
MEDIA STATISTIKA Vol 7, No 1 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Wheat as a base substance of flour, is a source of carbohydrate which is most used for the manufacturing of variety of foodstuffs. Substitution a part of flour with composite flour for manufacturing food will decrease dependency of imported wheat.This research aims to classify the area which produce base substance of composite flour in Indonesia.For this research we will know a group of provinces which become center of production and development target of local resources potency. One way that is used to grouping the object is cluster analysis. In development, there is another grouping technique used, namely Latent Class Cluster Analysis (LCCA).The results show that the selected model from grouping using LCCA is 3groups. The first group is the enough potential area as a production development center. While the second group have the greatest potential area. Meanwhile the last group is the less potentially area. Â Keywords: Composite Flour, Cluster Analysis, Latent Class Cluster Analysis (LCCA) Â

ANALISIS KLASIFIKASI MASA STUDI MAHASISWA PRODI STATISTIKA UNDIP dengan METODE SUPPORT VECTOR MACHINE (SVM) dan ID3 (ITERATIVE DICHOTOMISER 3) Ispriyanti, Dwi; Hoyyi, Abdul
MEDIA STATISTIKA Vol 9, No 1 (2016): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Graduation is the final stage of learning process activities in college. Undergraduate study period in UNDIPâ€™s academic regulations is scheduled in 8 semesters (4 years) or less and maximum of 14 semesters (7 years). Department of Statistics is one of six departments in the Faculty of Science and Mathematics UNDIP. StudyÂ period in this department can be influenced by many factors. Those factor are Grade Point Average (GPA) or IPK, gender, scholarship, parttime, organizations, and university entrance pathways. The aim of this paper is to determine the accuracy factors classification. We use SVM (Support Vector Machine method) and ID3 (Iterative Dichotomiser 3). The comparison of SVM and ID3 method, both for training and testing the data generate good accuracy, namely 90%. Especially ID3 training data gives better result than SVM.Â Keywords: Â SVM, ID3

PEMILIHAN VARIABEL PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION Yasin, Hasbi
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis that aims to model the relationship between response variable with some predictor variables. Geographically Weighted Regression (GWR) is statistical method used for analyzed the spatial data in local form of regression. One of the problems in GWR is how to choose the significant variables. The number of predictor variables will allow the violation of assumptions about the absence of multicollinearity in the data. Therefore, this needs a method to reduce some of the predictor variables which not significant to the response variable. This paper will discuss how to select significant variables by stepwise method. This method is a combination of forward selection method and the backward elimination method. Keywords: Â Geographically Weighted Regression, Backward Elimination, Forward Selection, Stepwise Method

PEMODELAN PERTUMBUHAN EKONOMI DI PROVINSI BANTEN MENGGUNAKAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION Hasbi Yasin; Budi Warsito; Arief Rachman Hakim
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Economic growth can be measured by amount of Gross Regional Domestic Product (GRDP). Based on official news of statistics BPS, Economic growth in Banten region has increase up to 5.59%. It supported by several sector, there are agriculture, business, industry and from various fields. Mixed Geographically Weighted Regression (MGWR) methods have been developed based on linear regression by giving spatial effect or location (longitude and latitude), the resulting model from Economic growth in Banten will be local or different based on each location. MGWR mixed method between linear regression and GWR, parameters in linear regression are global and GWR parameters are local. The results more specific because economic growth in Banten region assessed by location.Keywords: Banten, Economic growth, MGWR.

PEMODELAN REGRESI PROSES GAUSSIAN PEMODELAN REGRESI PROSES GAUSSIAN MENGGUNAKAN FUNGSI PERAGAM EKSPONENSIAL KUADRAT Mukid, Moch. Abdul
MEDIA STATISTIKA Vol 3, No 1 (2010): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Gaussian Process is a collection of random variables where any finite subset of that has a joint multivariate Gaussian distribution. A Gaussian Process is fully specified by its mean and its covariance function. One of the popular covariance functions is squared exponential that has two hyperparameters. In this paper Gaussian Process is used to made a prediction of Â the number of clothes produced by PT. APAC INTI CORPORA based on the number of attending employes, the number of overtime employes, the number of brokendown machines and used materials. Â Â Keywords: Gaussian Process, Covariace Functions, Squared Exponential

MODEL DEBIT DAERAH ALIRAN SUNGAI JANGKOK BERDASARKAN HASIL PREDIKSI MODEL STATISTICAL DOWNSCALING NONPARAMETRIK KERNEL CURAH HUJAN DAN TEMPERATUR Mustika Hadijati; Irwansyah Irwansyah
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

River water discharge is influenced by climatic conditions. River water discharge is important information for water resources management planning, so it is necessary to develop river water discharge model as basis of its predictions. In order to get the result of predictions of river water discharge with high accuracy, it is developed a model of river water discharge based on the predictions of local climate (local rainfall and temperature) that are influenced by global climate conditions..Prediction of local climate is based on the Kernel nonparametric statistical downscaling model by utilizing GCM data. GCM data is a high dimensional global data, so data pre-processing is needed to reduce data dimension. It is done by CART algoritm. Statistical downscaling model is used to predict local rainfall and temperature. The prediction results are quite good with relatively small RMSE value. They are used to develop model of river water discharge. Modeling river water discharge is carried out using the Kernel nonparametric approach. The model of river water discharge produced is quite good because it can be used to predict river water discharge with relatively small RMSE.

PROSES ANTRIAN DENGAN KEDATANGAN BERDISTRIBUSI POISSON DAN POLA PELAYANAN BERDISTRIBUSI GENERAL Sugito, Sugito; Hoyyi, Abdul
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In the queuing process,Â Â the distribution testing is performed to obtain the distribution of arrival and service distributions. Customer arrival distribution is obtained based on the number of arrivals or inter-arrival time. Service distribution is obtained based on the number of arrivals or inter-arrival time. In this paper we will discuss the process in queuing with the arrival of the Poisson distribution and the general pattern of service distribution Â Keywords : Queuing,Â Arrival Distribution, Service Distribution

PERAMALAN PENGGUNAAN BEBAN LISTRIK JANGKA PENDEK GARDU INDUK BAWEN DENGAN DSARIMA Saptyani, Marita; Sulandari, Winita; Pangadi, Pangadi
MEDIA STATISTIKA Vol 8, No 1 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bawen substation is a part of electrical distribution system. Forecasting load demand is required for power planning. Data used in this research are an hourly load demand of Bawen, Salatiga for 3 months, from February 2, 2013 to April 29, 2013, measured in Megawatt (MW).A half hourly load demand forecasting is needed for real time controlling and short-term maintenance schedulling. Since the data have two seasonal periods, i.e. daily and weekly seasonality with length 48 and 336 respectively, the model of double seasonal ARIMA (DSARIMA) is proposed as the most appropriate model for the case. Initial model is determined by the pattern of the data, based on the autocorrelation function plot. Some experiments was done by choosing several periods data. The most suitable model is chosen based on the outsample mean absolute percentage error (MAPE). The current study shows that the DSARIMA (0, 1, [1, 20, 47])(0, 1, 1)48(0, 1, 0)336 is the best model to forecastÂ 336 next period.Â Keywords: DSARIMA, MAPE, Electricity, Bawen

ANALISIS KUALITAS PELAYANAN DAN PENGENDALIAN KUALITAS JASA BERDASARKAN PERSEPSI PENGUNJUNG Sudarno, Sudarno; Rusgiyono, Agus; Hoyi, Abdul; Listifadah, Listifadah
MEDIA STATISTIKA Vol 4, No 1 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of the factors which determine customer safisfaction is costumer perceive about service quality that focus to five service quality dimension that are tangible, reliability, responsiveness, assurance, and empathy. This research study service serve quality at UPT Perpustakaan Universitas Diponegoro Semarang with object to know customer perceive with respect to some variables in service quality dimension and satisfaction level. Importance-Performance Analysis used to map relation between importance with performance of respective variables to be and see gap between performance with importance of them variables. Customer Satisfaction Index (CSI) used to analyze all satisfaction respondent level. The T2 Hotelling control chart to know servicing process stability with respect to costumer perceive. Research result shows that the gap is all negative value. It means library performance that represented by 21 variables include 5 service quality dimension still under expected costumer. The value CSI is 62,903% that meaning at enough satisfaction criterion. There are five points at above upper control limit in the T2 Hotelling control chart. Therefore it can be said that process havenâ€™t been controlled by statistical. Â Keywords: Service Quality, Importance-Performance Analysis, Customer Satisfaction Index,Â Hotelling T2 Control Chart.

MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL Arief Rachman Hakim; Hasbi Yasin; Agus Rusgiyono
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Central Java in 2017 was one of the provinces with high life expectancy, ranking second. Life expectancy of Central Java Province in 2017 is 74.08% per year. The fields of education, health and socio-economics, are several factors that are thought to influence the life expectancy in an area. To find out what factors that the regression analysis method can use to find out what factors influence the life expectancy. But in observations found data that have a spatial effect (location) called spatial data, a spatial regression method was developed such as linear regression analysis by adding spatial effects. One form of spatial regression is Spatial Durbin Model (SDM) which has a form like the Spatial Autoregressive Model (SAR). The difference between the two if in the SAR model the effect of spatial lag taken into account in the model is only on the response variable (Y) but in the SDM method, effect of spatial lag on the predictor variable (X) and response (Y) are also taken into account. Selection of the best model using Mean Square Error (MSE), obtained by the MSE value of 1.156411, the number mentioned is relatively small 0, which indicates that the model is quite good.

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