Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

Media Statistika

media_statistika
Website

Published by Universitas Diponegoro

ISSN : - EISSN : 24770647 DOI : -

Core Subject : Science,

Decision Sciences, Operations Research & Management

Arjuna Subject : -

Articles 6 Documents

Search results for , issue "Vol 6, No 1 (2013): Media Statistika" : 6 Documents clear

ESTIMASI PARAMETER MODEL MIXTURE AUTOREGRESSIVE (MAR) MENGGUNAKAN ALGORITMA EKSPEKTASI MAKSIMISASI (EM) Asrini, Mika; Sulandari, Winita; Wiyono, Santoso Budi
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Mixture autoregressive (MAR) Model is a mixture of Gaussian autoregressive (AR) components. The mixture model is capable for modelling of nonlinear time series with multimodal conditional distributions. This paper discusses about the parameters estimation using EM algorithm. All possible models are then applied to national maize production data. In this case, the BIC is used for the MAR model selection. Keywords : Mixture Autoregressive, EM Algorithm, BIC, Maize Production

PEMETAAN PENYAKIT DEMAM BERDARAH DENGUE DENGAN ANALISIS POLA SPASIAL DI KABUPATEN PEKALONGAN Yasin, Hasbi; Saputra, Ragil
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The number of dengue haemorrhagic fever (DHF) incidence in Pekalongan from year to year is very volatile. In 2006, there was 352 cases, 718 cases occurred in 2007, 2008 saw 403 cases, 2009 there were 753 cases, whereas in 2010 a decline to 223 cases. This is possible due to the lack of information about the place, time and location of the incident spread of dengue in Pekalongan. Various efforts have been made to address these issues both society and government but the incidence of this disease has not been effectively suppressed. The results of data analysis showed that the incidence of dengue in Pekalongan mostly occurs during the rainy season is the period from January to June. The DHF incidence tends to be higher in Kedungwuni. Highest incidence of DHF occurred in April 2010. In addition, there are some months that indicate the spatial relationships in the incidence of dengue in Pekalongan, ie January, February, July, October and December. The sub-district that has a positive autocorrelation isÂ Kedungwuni, Wonopringgo, and Tirto. While the sub-district has a negative autocorrelation is Karangdadap. Most of the sub-districts in Pekalongan status is still endemic for dengue. Â Keywords: DHF, Moranâ€™s Index, Spatial Pattern

PEMODELAN DATA KEMISKINAN DI PROVINSI SUMATERA BARAT DENGAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) Maggri, Ilham; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Counting the number of poor have often been modeled as a function of a global regression, which meant that the regression coefficient value applied to all geographic regions. Though this assumption was not always valid because of the differences in geographic locations most likely causing the spatial heterogeneity. In case of spatial heterogeneity, the regression parameters would vary spatially, so if the global regression model was applied, would produce an average value of those regression parameters which vary spatially. This study uses the method Geographically Weighted Regression (GWR) to analyze data that contains spatial heterogeneity. In GWR model estimation, the model parameters are obtained by using the Weighted Least Square (WLS) which gives a different weighting in each location. This study discusses the factors that influence the level of poverty in the province of West Sumatra. Suitability test of the model results shows that there is no influence of spatial factors on the level of poverty in the province of West Sumatra. The results shows that there are four variables that are assumed to affect the level of poverty in the province of West Sumatra, they are the variable of floor space, the facility to defecate, ability to pay the cost of health center / clinic and educationÂ levels of household head. The four variables have a similar effect in every city and county.Keywords : Poverty, Spatial Heterogeneity, Geographically Weighted Regression

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

ANALISIS DATA INFLASI DI INDONESIA MENGGUNAKAN MODEL REGRESI SPLINE Suparti, Suparti
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. The developed model of parametric to cope with the volatility of the data is the ARCH and GARCH models. This alternative parametric models still requires the normality assumption in the data that often cannot be satisfied by financial data. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This research aims to conduct a study in Indonesia inflation data modeling using nonparametric methods is spline regression model with truncated spline bases. Goodness of a spline regression model is determined by an orde and knots location . However, the knots location are more dominant in spline regression model. One way to get the optimal knots location are by minimizing the value of Generalized Cross Validation (GCV). By modeling the annual inflation data of Indonesia in December 2006 - December 2011, the inflation target in 2012 is 4.5% + 1% can be achieved while the inflation target in 2013 is 4.5% + 1% cannot be achieved, because that prediction in 2013 is 8.55%. It was caused by government policy to raise the price of basic electricity and the fuel prices in 2013. Keywords : Inflation, Spline Regression Model, Generalized Cross Validation.

MODEL REGRESI COX PROPORSIONAL HAZARD PADA DATA KETAHANAN HIDUP Hanni, Tuan; Wuryandari, Triastuti
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

A lot of events occured in daily life are connected with survival time, for example a time interval that measure the failure of a product, time duration which is needed to recover from disease, the back pain recurred after treatment. Data about survival time duration of an event is called survival data. Survival data can not be observed completely that is called as sensored data. Cox proportional hazard model is employed to analyze and determine the survival rate from cencored data affected one or more explanatory variables. This model assummed that the hazard rate of group is proportional to the hazard rate of another group. In the paper, wants to the factor that affect the survival of patient with cervical cancer. From the result of data processing that affect are age and stadum with cox proportionl hazard model isÂ hi(t) = exp(-1.848U1i â€“ 1.584U2i â€“ 3.255S2i - 2.108S3i ) h0(t) Â Keywords : Cox Proportional Hazard, Survival Rate, Hazard Rate, Cervical Cancer

Page 1 of 1 | Total Record : 6

Filter by Year

2013 2013

Filter By Issues

All Issue Vol 18, No 1 (2025): Media Statistika Vol 17, No 2 (2024): Media Statistika Vol 17, No 1 (2024): Media Statistika Vol 16, No 2 (2023): Media Statistika Vol 16, No 1 (2023): Media Statistika Vol 15, No 2 (2022): Media Statistika Vol 15, No 1 (2022): Media Statistika Vol 14, No 2 (2021): Media Statistika Vol 14, No 1 (2021): Media Statistika Vol 13, No 2 (2020): Media Statistika Vol 13, No 1 (2020): Media Statistika Vol 12, No 2 (2019): Media Statistika Vol 12, No 1 (2019): Media Statistika Vol 11, No 2 (2018): Media Statistika Vol 11, No 1 (2018): Media Statistika Vol 10, No 2 (2017): Media Statistika Vol 10, No 1 (2017): Media Statistika Vol 9, No 2 (2016): Media Statistika Vol 9, No 1 (2016): Media Statistika Vol 8, No 2 (2015): Media Statistika Vol 8, No 1 (2015): Media Statistika Vol 7, No 2 (2014): Media Statistika Vol 7, No 1 (2014): Media Statistika Vol 6, No 2 (2013): Media Statistika Vol 6, No 1 (2013): Media Statistika Vol 5, No 2 (2012): Media Statistika Vol 5, No 1 (2012): Media Statistika Vol 4, No 2 (2011): Media Statistika Vol 4, No 1 (2011): Media Statistika Vol 3, No 2 (2010): Media Statistika Vol 3, No 1 (2010): Media Statistika Vol 2, No 2 (2009): Media Statistika Vol 2, No 1 (2009): Media Statistika Vol 1, No 2 (2008): Media Statistika Vol 1, No 1 (2008): Media Statistika More Issue

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
-

Location
Kota semarang,

Jawa tengah

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name -

Contact Email -

Phone -

Journal Mail Official -

Editorial Address -

Location Kota semarang, Jawa tengah INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
-

Location
Kota semarang,

Jawa tengah

INDONESIA