http://journal.stats.id/index.php/ijsa/issue/feedIndonesian Journal of Statistics and Its Applications2018-11-12T11:20:00+00:00Agus M Solehagusms@apps.ipb.ac.idOpen Journal Systems<p><strong>Indonesian Journal of Statistics and Its Applications (<a href="http://u.lipi.go.id/1510202061">eISSN:2599-0802</a>):</strong> diterbitkan berkala 2 (dua) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika dan aplikasinya. Artikel yang dimuat berupa hasil penelitian bidang statistika dan aplikasinya dengan topik (tapi tidak terbatas): rancangan dan analisis percobaan, metodologi survey dan analisis, riset operasi, data mining, pemodelan statistika, komputasi statistika, time series dan ekonometrika, serta pendidikan statistika.</p> <p> </p>http://journal.stats.id/index.php/ijsa/article/view/53POISSON REGRESSION OF DAMAGE PRODUCT SALES USING MCMC2018-11-12T11:20:00+00:00Reny Rian Marlianarenyrianmarliana@gmail.comSeptiadi Padmadisastras_padmadisastra@yahoo.com<p>In this paper a model for the number of “damage” product sales is studied. The product sales are run into underreporting counts, caused by a delay on input process of the system called sales cycle. The goal of the study is to estimate the parameters of the regression model of product sales on an explanatory variable. It is the actual number of product sales. The model used is a mixture of the Poisson and the Binomial distributions. The parameters of the regression model are estimated by a Bayesian approach and Markov Chain Monte Carlo simulation using Gibbs sampling algorithm. The results of estimation clearly showed a gap between undamage product sales and the actual number. The gap is the number of damaged product sales.</p>2018-04-30T00:00:00+00:00##submission.copyrightStatement##http://journal.stats.id/index.php/ijsa/article/view/55ALTERNATIF PENGGEROMBOLAN DATA DERET WAKTU DENGAN KONDISI TERDAPAT DATA KOSONG2018-11-12T11:19:57+00:00Yusma Yantiyusma.yanti@unpak.ac.idSeptian Rahardiantororahardiantoro.stk@gmail.com<p>Panel data describes a condition in which there are many observations with each observation observed periodically over a period of time. The observation clustering context based on this data is known as Clustering of Time Series Data. Many methods are developed based on fluctuating time series data conditions. However, missing data causes problems in this analysis. Missing data is the unavailability of data value on an observation because there is no information related to it. This study attempts to provide an alternative method of clustering observations on data with time series containing missing data by utilizing correlation matrices converted into Euclid distance matrices which are subsequently applied by the hierarchical clustering method. The simulation process was done to see the goodness of alternative method with common method used in data with 0%, 10%, 20% and 40% missing data condition. The result was obtained that the accuracy of the observation bundling on the proposed alternative method is always better than the commonly used method. Furthermore, the implementation was done on the annual gini ratio data of each province in Indonesia in 2007 to 2017 which contained missing data in North Kalimantan Province. There were 2 clusters of province with different characteristics.</p>2018-04-30T00:00:00+00:00##submission.copyrightStatement##http://journal.stats.id/index.php/ijsa/article/view/61REGRESI POISSON BIVARIAT DENGAN KOVARIAN MERUPAKAN FUNGSI DARI VARIABEL BEBAS2018-11-12T11:19:52+00:00Untung Kurniawanuntungk@bps.go.id<p>Poisson regression is a regression model which often used to analyze the count data. In this study, poisson regression has been used bivariate poisson regression where the regression is a method which is used to model a pair of correlated count data with multiple predictor variables. The model is used covariance which has a function of the independent variable. The purposes of this study is obtain parameter estimates, test statistics of bivariate poisson regression, and determine the factors that influence of infant mortality and maternal mortality. The data is used from the infant mortality and maternal mortality in Central Java 2015. Based on the result, the parameter estimation of poisson bivariate regression model using maximum likelihood (MLE) method. The results obtained from the parameter estimation are not close form so it needs to be done by Newton-Raphson iteration method. In testing the hypothesis using the Maximum Likelihood Ratio Test method (MLRT) by comparing the value between likelihood below H<sub>0</sub> and likelihood below population. Partial of parameters model λ<sub>1</sub> (infant mortality) there are six independent variables that have significant influence, namely, delivery by health personnel (X<sub>1</sub>), pregnant women carry out the program K4 (X<sub>3</sub>), pregnant women who get Fe3 tablet (X<sub>4</sub>), handling obstetric complication (X<sub>5</sub>), exclusively breastfed infants (X<sub>7</sub>), and households living a clean and healthy life (X<sub>8</sub>). While for model λ<sub>2</sub> (maternal death) only variable handling of neonatal complication (X<sub>6</sub>) which have no significant influence to response variable.</p>2018-04-30T00:00:00+00:00##submission.copyrightStatement##http://journal.stats.id/index.php/ijsa/article/view/64KAJIAN SIMULASI PENDUGAAN SELANG KEPERCAYAAN BOOTSTRAP BAGI ARAH MEDIAN DATA SIRKULAR2018-11-12T11:19:50+00:00Cici Suhaenicici_suhaeni@apps.ipb.ac.idI Made Sumertajayaimsjaya@yahoo.comAnik Djuraidahanikdjuraidah@gmail.com<p>The median direction is one of central tendency of circular data. The estimation process usually requires information about sampling distribution of statistic that want to be used as a parameter estimate. Theoretically, sampling distribution derived from population distribution. But, it is not easy to get sampling distribution of median although the population distribution is known. When the sampling distribution cannot be derived easily from population distribution, the bootstrap method can be an alternative to handle it. This study wants to evaluate the effect of increasing concentration parameter to the performance of bootstrap confidence interval estimation for median direction through simulation study. Three methods were used to estimate the interval which are equal-tailed arc (ETA), symmetric arc (SYMA), and likelihood-based arc (LBA). The most important criterion to evaluate them were true coverage and interval width. The simulation results that in general, the increasing of concentration parameter followed by more narrow interval. For small concentration parameter (k<1), all methods give unstable true coverage and interval width. The authors also identify that those three methods produce intervals with identical width when the parameter concentration is 20 or more. In terms of coverage and interval width, the best method was ETA.</p>2018-04-30T00:00:00+00:00##submission.copyrightStatement##http://journal.stats.id/index.php/ijsa/article/view/57FAKTOR-FAKTOR YANG MEMENGARUHI UNMET NEED KB DI PROVINSI BENGKULU TAHUN 2015 DENGAN PEMODELAN REGRESI LOGISTIK BINER2018-11-12T11:19:54+00:00Abyan Raiabyanrai29@gmail.comReza Rizky Ramadhan15.8847@stis.ac.id<p>Indonesia failed to reach the target to reduce the percentage of unmet need KB in 2015. Province of Bengkulu which has almost 20% of family planning services from government, has a high unmet need percentage. The purpose of this research is to determine the factors that affect status of unmet need KB of women aged 15-49 years in Bengkulu 2015. Data in this study obtained from Susenas 2015. The result showed that 21,34% of women aged 15-49 years in Bengkulu in 2015 was unmet need KB and 78,65% was not unmet need KB. Using binary logistic regression, the result showed that the age of women, number of surviving children, education of women aged 15-49 years, and type of residence have a significant effect on status of unmet need KB. Socialization of family planning program on Bengkulu is needed to reduce the percentage of unmet need KB of women aged 15-49 years on Bengkulu. Further research is suggested to use other independent variables and see spatial correlation in Province of Bengkulu.</p>2018-04-30T00:00:00+00:00##submission.copyrightStatement##