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INDONESIA
FORUM STATISTIKA DAN KOMPUTASI
Published by Institut Pertanian Bogor
ISSN : 08538115     EISSN : -     DOI : -
Core Subject : Education,
Mathematics
Forum Statistika dan Komputasi (ISSN:0853-8115) was published scientific papers in the area of statistical science and the applications. It is issued twice in a year. The papers should be research papers with, but not limited to, following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education.
Arjuna Subject : -
Articles 119 Documents
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GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) INCLUDED THE DATA CONTAINING MULTICOLLINEARITY Ira Yulita; Anik Djuraidah; Aji Hamin Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

One of the reasons of spatial effect of each location is spatial variety. Beside of spatial variety, number of independent variable (X) causes local multicolinearity, that is one or more independent variable, which collaborated with other variable in each location of observation. The methods can be used to solve spatial diversity problem and local multicollinearity in Geographically Weighted Regression (GWR) model that is GWPCA. This research aim to examine GWPCAR feasibility model for PDRB data in 2010 at 113 districts/cities in Java. analysis indicate that GWPCA method can overcome local multicollinearity problem, it can be seen from the characteristic value of VIF which is smaller than 10.Key words : Local Multicollinearity, Geographically Weighted Principal Components Analysis.
RANDOM PARAMETER MODELS OF FERTILIZER RESPONSE FOR CORN USING SKEWED DISTRIBUTIONS Mohammad Masjkur
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
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Abstract

Random parameter models have been found to better determine the optimum dose of fertilizer than fixed parameter. However, a major restriction of it is the normality assumption.. The purpose of this study the introduction of random parameter models of fertilizer response using skewed distributions from a Bayesian perspective. The method is applied to data sets of multilocation trials of potassium fertilization on corn. We compare the Linear Plateau, Spillman-Mitscherlich, and Quadratic random parameter models with different random errors distribution assumption, i.e. as normal, skew-normal, Student-t and Skew-t distribution using the Deviance Information Criterion (DIC). The results show that the smallest DIC value is obtained for the normal linear plateau model compare with the other models. The correlation between observed and fitted values was significant.Key words : fertilizer response model, mixed effects, skewed distributions, DIC.
AUTOREGRESSIVE MOVING AVERAGE (ARMA) MODEL FOR DETECTING SPATIAL DEPENDENCE IN INDONESIAN INFANT MORTALITY DATA Ray Sastri; Khairil Anwar Notodiputro; _ Indahwati
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
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Abstract

Infant mortality is an important indicator that must to be monitored seriously. The infant mortality is associated with several determinants, such as the infant’s characteristics, maternal and fertility factors, housing condition, geographical area, and policy. It can also be influenced by the presence of spatial dependence between regency in Indonesia. This is due to the social and economic activity in one regency depend on social and economic activity in other regency, especially with neighboring area. Infant mortality data obtained from Indonesian Demographic and Health Survey (IDHS) published by Statistic Indonesia (BPS). In BPS’s publication, data is always sorted by regency code from the smallest to the largest. Therefore, the closeness of the regency code refers to the closeness of the regency itself. the infant mortality data by regency could be analogized as time series data. So that, the relationship between regency can be seen using Autoregressive Moving Average (ARMA) model. If the parameter at ARMA is significant, we can conclude that there is a spatial dependence on the infant mortality in Indonesia. This paper will focus on discussing whether there is a spatial dependenc in Indonesia’s Infant Mortality Data using ARMA approach. The result is the Autocorrelation Function (ACF) showed a significant effect until lag 3, and Partial Autocorrelation Function (PACF) showed a significant effect until lag 1. Based on Bayesian Information Criterion (BIC), the AR(1) fitted the model well. It shows that the probability of infant mortality in one regency is affected by probability of infant mortality in neighboring regency.Key words : ARMA, spatial dependence, infant mortality, IDHS
TEMPERATURE CHANGES IN CLOUD FOREST OF KHAO NAN NATIONAL PARK, SOUTHERN THAILAND DURING 2000 - 2015 Anusa Suwanwong; Noodchanath Kongchouy
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
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Abstract

Khao-Nan National Park(KNNP) is a part of the Nakhon Si Thammarat mountain range, which is the one of the cloud forest southern Thailand. The characteristic of cloud forest is a plenty of flora especially epiphyte and the presence of clouds even in the dry season. The aim of this study was to investigate temperature pattern and variation at Khao Nan. We downloaded data, for land surface temperatures recorded by MODIS EARTH Satellites every eight days from 2000-2015 in square kilometers grid boxes covering Khao-Nan National Park, to investigate time series of temperature variation. The cubic spline modeling was used for fitting a pattern of LST among day time from satellite image at Khao-Nan National Park. Otherwise, we used GEE for parameter estimate. The result was shown the temperature has similar pattern and variation around Khao-Nan National Park during 2000-2015. Eventually, the conclusion is the temperature have changed during 2000-2005, 2006-2009 and 2010-2015 by using GEE.Key words : Temperature changes, cloud forest, Khao Nan National Park
ADVECTION-DIFFUSION MODEL WITH TIME DEPENDENT FOR AIR POLLUTANTS DISTRIBUTION IN UNSTABLE ATMOSPHERIC CONDITION Syafika Ulfah; Collins Bekoe; Benjamin Atta Owusu
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
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Abstract

Air pollution levels are quite high in urban areas. They are emitted from various sources and have an impact on humans and the environment. There are some physical processes that occur when pollutants disperse in the atmosphere. The main processes are advection and diffusion. Therefore, a two-dimensional mathematical model is presented to study the dispersion of air pollution under the effect of mesoscale wind as an effect of urban heat islands. This model is solved by using the implicit Crank-Nicolson finite difference scheme under stability-dependent meteorological parameters involved in large scale wind, mesoscale wind and eddy diffusivity. The main goal of this research is to analyze air pollution distribution using the advection-diffusion model. The results of this model have been analyzed for the dispersion of air pollutants in an urban area in the downwind and vertical direction for unstable atmospheric conditions.Key words : Advection, Diffusion, Mesoscale Wind, Pollutant Dispersion
COMPARISON OF LOW BIRTH WEIGHT RATE ESTIMATES BASED ON DIFFERENT AGGREGATE LEVELS DATA USING LOGISTIC REGRESSION MODEL Antonius Benny Setyawan; Khairil Anwar Notodiputro; _ Indahwati
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
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Abstract

Low Birth-Weight (LBW) is defined as a birth weight of a live-born infant of less than 2.500 grams regardless of gestational age. Case of LBW is associated with infant mortality, infant morbidity, inhibited growth and slow cognitive development, also chronic diseases in later life. It is vital because with high LBW rate the generation hardly grow into its full potential. There are many risk factors, whether direct or indirect, can cause a birth as a high risk of Low Birth Weight case. These factors are genetics, obstetrics, nutrition intakes, diseases, toxic exposures, pregnancy care and social factors. With these factors measured, statistical modelling can be used to estimate rate on group level or probability on individual level of the Low Birth Weight event. As the case is a binary response, Logistic Regression Model is commonly used.Data of LBW case and the risk factors came from Indonesian Demographic and Health Survey (IDHS) 2012. Published national rate of LBW was 7.3% with provincial rates fell between 4.7-15.7 %. Although the national rate was considered low, the wide variation of provincial rates showed that the problem was not handled so well. However, these rates cannot be measured yearly due to 5 year period of the survey. With the availability of risk factors data a model can be built to estimate the LBW rates. But, another problem for the model is the case when aggregate level data is available instead of individual level data. So, the purpose of this study was to compare models based on different aggregate levels and theirs estimated provincial rates. Comparison was done among individual birth level, mother level, household level and census block (cluster) level. Models from three former levels were quite similar with adequate significant parameters, while cluster level model was resulted only a few significant parameters. But instead, LBW rate estimates from cluster level model were the closest to the direct estimates. But the variance of these estimates was still higher than the other models.Key words : Low Birth-Weight, IDHS, Logistic Regression, GLM, Aggregate Data
EBLUP METHOD OF TIME SERIES AND CROSS-SECTION DATA FOR ESTIMATING EDUCATION INDEX IN DISTRICT PURWAKARTA Febriyani Eka Supriatin; Budi Susetyo; Kusman Sadik
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
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Abstract

Since decentralisation was implemented in Indonesia, more detailed information about the condition of an area becomes very necessary to know as an evaluation of development that the government has done. the success development of a region can be seen through the Human Development Index (HDI). HDI consists of three basic dimensions, knowledge as one of that three basic measured by the index of education. This index is measured by the Adult Literacy Rate and Mean Years of Schooling. Education is one of the important factors in improving human development. The enhancement of education index results in increasing the HDI of an area. Purwakarta has a vision that is made as a district that excels in education in West Java, but until now Purwakarta’s education index is still below the West Java province. One step that can be done is to seek information on the education index each district in Purwakarta, with the aim to provide the right policy in each region. Direct estimation of the components forming the HDI for districts is not feasible because these estimates will generate a great value of variance, This is due to the size of the sample used is too small. This study proposes a statistical method by performing the estimation using small area estimation. These estimates using information from surrounding areas that can improve the effectiveness of the sample size and the lower the standard error. Some surveys are conducted regularly every year, in conducting indirect estimation in the survey such as this, efficiency of estimating education index for district level can be improved by including the random effect of the area as well as the random effect of time (Sadik and Notodipuro, 2006). So in this study will be used Empirical Best Linear Unbiased Prediction (EBLUP) by combining the time series and cross-section data for estimating the education index at the level of districts in Purwakarta. The direct estimation of education index produce a larger variance than our methode, it shown by comparing mean square error (MSE) of direct method and indirect method, direct method have the largest MSE.Key words : Indirect Estimation, Small Area Estimator, EBLUP, Time Series and Cross-Section, HDI, Education Index.
MODELING OF DENGUE HEMORRHAGIC FEVER IN BOGOR USING BAYESIAN SUR-SAR Hilman Dwi Anggana; Asep Saefuddin; Bagus Sartono
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
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Abstract

The purposes of this research are (1) To develop Seemingly Unrelated Regression (SUR) system constructed by correlated Spatial Autoregressive Model (SAR) with Bayesian approach for dynamic analysis of spatial and non-spatial contributions of Dengue Hemorrhagic Fever (DHF) case in Bogor, (2) To evaluate efficiency issues on parameters estimation with SUR system. Markov Chain Monte Carlo (MCMC) sampling scheme was used to estimate all of model parameters with the number of iteration whose burn-in period was discovered. The results indicated that : there was the similar pattern of DHF spread in Bogor during 2009 – 2011, the nearby areas had a significant role to the incidence of DHF in an area in the city of Bogor, and the non-spatial contributions of DHF cases in Bogor during 2009 -2011 included in this model were dynamic. Gain efficiency of parameters estimation on modeling of DHF in Bogor with SAR for each year during 2009-2011 can be obtained if we construct all of SAR with SUR system model.
SMALL AREA ESTIMATION OF LITERACY RATES ON SUB-DISTRICT LEVEL IN DISTRICT OF DONGGALA WITH HIERARCHICAL BAYES METHOD Rifki Hamdani; Budi Susetyo; _ Indahwati
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
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Abstract

Literacy Rate (LR) is defined as percentage of population aged over 15 with ability to read and write. LR, as one of people welfare indicators, is a measurement of educational development. The indicator, as a measurement of government performance on education, can be measured if all variables related is available. Statistics Indonesia (BPS) each year calculated LR based on National Socio-Economic Survey (SUSENAS) with estimation available only on provincial level and district level. Along with establishment of autonomous regional policy, where regional government had greater power to manage its own region, availability of LR on lower levels to monitor educational development is necessary. Due to sampling design of SUSENAS, accommodated only estimation on district level, will give high variance if used to estimate on lower sub-district level, although still unbiased. Modelling LR was done with Logit-Normal approach, because LR data followed Binomial Distribution. Good estimators from inadequate sample size can be obtained with method of Small Area Estimation (SAE). Hierarchical Bayes (HB) method is one of SAE methods which are proven to give good estimate on binomial distributed data as LR. Estimation on sub-district level in District of Donggala with HB method gave better result compared to the direct estimation with lower Mean Square Error (MSE).Key words : Small Area Estimation, Literacy Rate, Hierarchical Bayes, Logit-Normal Model
CLUSTERING PROVINCE IN INDONESIA BY COMMUNICATION TECHNOLOGY RELATED VARIABLES Ahmad Nur Rohman; _ Erfiani; Muhammad Nur Aidi
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
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Abstract

Technological developments in Indonesia growth rapidly. Almost all systems used in daily life have been using the technology. One of its technology is communication technology. It because communication technology is a important tool for send information. All was done in order to communicate easier and faster. It is therefore important to research the condition of the existing communication technology in Indonesia. Communications technology also one of the focus of the government in national development. But not easy to know the state of communication technology in Indonesia because Indonesia has a large region and different geographically. The purpose of this research was to determine the grouping of provinces in Indonesia to increase the communication sector in order to support national development. The method used in this research is cluster hierarchical analysis method and criterion of determining the best method and many cluster optimal use Cubic Clustering Criterion (CCC). The data used is secondary data from the Statisctics Indonesia (BPS) and the Ministry of Communication and Information. The results showed that the number of cluster based on related communication technology variables are 3 cluster which 1st cluster members consist of 21 provinces, 2nd cluster members consist of 7 provinces and 3rd cluster members consist of 3 provinces.Key words : Communications Technology, Cluster Analysis, Hierarchical Method, Cubic Clustering Criterion (CCC)

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