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Media Statistika

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Published by Universitas Diponegoro

ISSN : - EISSN : 24770647 DOI : -

Core Subject : Science,

Decision Sciences, Operations Research & Management

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Articles 6 Documents

Search results for , issue "Vol 11, No 1 (2018): Media Statistika" : 6 Documents clear

PENERAPAN REGRESI DATA PANEL PADA ANALISIS PENGARUH INFRASTRUKTUR TERHADAP PRODUKTIFITAS EKONOMI PROVINSI-PROVINSI DI LUAR PULAU JAWA TAHUN 2010-2014 Yosephine Magdalena Sitorus; Lia Yuliana
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

There is inequality between the economic growth of provinces in Java and outside of Java. The total area of Java is only 6,77% from total area of Indonesia but the Growth Domestic Product (GDP) based on constant price in 2014, Java contributed 57,8% of the GDP total Indonesia. One cause that made this disparity is the development of infrastructure in outside Java is still weak. The development of infrastructure is a basic element for increasing total output production that later will increase the economic growth. However, there are so many problems that occur in developing the infrastructure in outside of Java. This study aimed to analyze the condition of infrastructure provinces outside Java in 2010-2014. The data used is the secondary data for 27 provinces outside of Java 2010-2014 from BPS. The analytical method used is panel data regression with fixed effect model and Seemingly Unrelated Regression (SUR) Model. Based on the results, the infrastructure that affects economic productivity significantly and positively is road infrastructure, health, and budget. Infrastructure that affects economic productivity significantly and negatively is the educational infrastructure. Water and electricity infrastructure did not significantly affect economic productivity.Keywords: Infrastructure, Economic productivity, Panel Data Regression, Fixed Effect Model

ANALISIS KEPUTUSAN NASABAH DALAM MEMILIH JENIS BANK: PENERAPAN MODEL REGRESI LOGISTIK BINER (STUDI KASUS PADA BANK BRI CABANG BALIKPAPAN) Saiful Ghozi; Ramli Ramli; Asri Setyani
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This paper analyze factors that influence customer preference between conventional and sharia bank, and which factor is the most dominant. The study was conducted in Balikpapan city from May 2017 until August 2017. The sample is 25 customers of BRI Sharia and 31 customers of conventional BRI. Statistical analysis model used in this paper is Binary Logistics Regression. There are 8 predictor variables to be analyzed to know their effect to customer decision in choosing bank between sharia bank and conventional bank. The variables are: knowledge of respondents about sharia bank (X1), knowledge of respondents about the difference between conventional and sharia banks (X2), knowledge of respondents about products offered by sharia bank (X3), promotion of sharia bank via printed media (X4), promotion of sharia bank via electronic media (X5), promotion of sharia bank in social activities (X6), the customer's efforts to observe religious orders (X7), and the customer's efforts to avoid the religious prohibitions (X8). The results of individual significance test indicate that knowledge of respondents about sharia bank, and promotion of sharia bank through electronic media has significant effect to the customer’s decision in choosing bank. And the most significant effect is promotion through electronic media (X5). Keywords : binary logistic regression, decision, sharia bank

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.

MODEL REGRESI POISON BIVARIAT DENGAN KOVARIAN KONSTAN Untung Kurniawan
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bivariate Poisson models are appropriate for modeling paired count data exhibiting correlation. This study aims to estimates the parameters and test hypothesis of bivariate Poisson regression on modeling the number of infant mortality and maternal mortality in Central Java 2015. The parameters of the bivariate regression model are estimated by using the maximum likelihood method. Results show that the percentage of births by health personnel, the percentage of pregnant women administered the K4 program, the percentage of pregnant women receiving Fe3 tablets, percentage of exclusively breastfed infants, and percentage of households behaved in a clean and healthy life are significant for the number of infant mortality in Central Java. The variables that have significant effect on maternal mortality are percentage of births by health personnel, percentage of maternal women receiving postpartum health services, and percentage of pregnant women receiving Fe3 tablets. Keywords: Bivariate Poisson Regression, Infant Mortality, Maternal Mortality, Maximum Likelihood Estimation

PENGARUH SKEWNESS DAN KURTOSIS DALAM MODEL VALUASI OBLIGASI Abdurakhman Abdurakhman; Di Asih I Maruddani
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Gram-Charlier expansion, where skewness and kurtosis directly appear as parameters, has become popular in finance as a generalization of the normal density. Non-normal skewness and kurtosis of underlying asset of bond issuer company are significantly contributes to the phenomenon of volatility smile. Hermite polynomial is used to get an expansion of the probability distribution. In this paper, Gram-Charlier model is applied to BTPN Bond which is issued in 2017. The result showed that Gram-Charlier model is more consistent than Black-Scholes model when the skewness and kurtosis are taken into account.Keywords: Skewness, Kurtosis, Gram-Charlier, Hermite polynomial

PEMODELAN HYBRID ARIMA-ANFIS UNTUK DATA PRODUKSI TANAMAN HORTIKULTURA DI JAWA TENGAH Tarno Tarno; Agus Rusgiyono; Budi Warsito; Sudarno Sudarno; Dwi Ispriyanti
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The research purpose is modeling adaptive neuro fuzzy inference system (ANFIS) combined with autoregressive integrated moving average (ARIMA) for time series data. The main topic is application of Lagrange Multiplier (LM) test for input selection, determining the number of membership function and generating rules in ANFIS. Based on partial autocorrelation (PACF) plot, the lag inputs which are thought have an effect to data are evaluated by using LM-test. Procedure of LM test is applied to determine the optimal number of membership functions. Based on the result, a number of rule-bases are generated. The best model is applied for forecasting potato production data in Central Java. The case study of this research is modeling monthly data of potato production from January 2004 up to December 2016. From empirical study, ANFIS optimal was obtained with lag-1 and lag-11 as inputs with two membership functions and two fuzzy rules. The hybrid method based on ARIMA and ANFIS is also implemented. The result of the prediction with a hybrid method is compared to the ANFIS and ARIMA. Based on the value of Mean Absolute Percentage Error (MAPE), hybrid model ARIMA-ANFIS has a good performance as a model of ANFIS and ARIMA individually.Keywords: Time Series, Potato production, hybrid, ANFIS, ARIMA, LM-test

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