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All Journal Jurnal Ilmu Pertanian Indonesia FORUM STATISTIKA DAN KOMPUTASI Media Statistika Disease Prevention and Public Health Journal JURNAL PETERNAKAN Sosioinforma JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications J-ADIMAS (Jurnal Pengabdian kepada Masyarakat) JURNAL AKUNTANSI KEUANGAN DAN MANAJEMEN Jurnal Pendidikan dan Kebudayaan Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Natural Xplore: Journal of Statistics Jurnal Pengabdian Masyarakat dan Lingkungan PRAGMATIS Jurnal Manajemen dan Bisnis Jurnal Medika Veterinaria Scientific Journal of Informatics Indonesian Journal of Statistics and Its Applications Jurnal Info Kesehatan
Indahwati
Department Of Statistics, Faculty Of Mathematics And Natural Science, IPB University, Bogor, Indonesia
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Journal : FORUM STATISTIKA DAN KOMPUTASI

 1 
APLIKASI REGRESI LOGISTIK ORDINAL MULTILEVEL UNTUK PEMODELAN DAN KLASIFIKASI HURUF MUTU MATA KULIAH METODE STATISTIKA Iin Maena; Indahwati .; Dian Kusumaningrum
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 2 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Statistical Methods  (STK211) is an interdept course under coordination of  Statistic Departement Faculty of  Mathematics and Natural Science, Bogor Agricultural University (BAU). The final grade received by student  who follow Statistical Methods is measurement  in ordinal scale,  that is A, B, C, D and E.  In the 2008/2009 academic  year  there  are  7  parallel  classes  in  the  Faculty of  Mathematics  and  Natural  Science,  BAU.  By considering the hierarchical structure contained  in the score of student achievement data, the student (first level) is  nested in a parallel class (second level), hence this study used multilevel ordinal logistic regression analysis  to  model  the  final  score  of  Statistical  Methods  with  the  factors  that  influence  it.  Explanatory variables that significantly affect the final score of Statistical Methods are the GPA of TPB (student’s first year of college) and gender, with the variability of the intercepts across parallel classes in the logit function as 1.184. Percentage classification accuracy obtained by using multilevel ordinal logistic regression model was 56.85%Keywords : hierarchical, multilevel modeling, multilevel ordinal logistic regression, classification
KAJIAN SIMULASI KETAKNORMALAN PENGARUH ACAK DAN BANYAKNYA DERET DATA LONGITUDINAL DALAM PEMODELAN BERSAMA (JOINT MODELING) (Simulation Study of Random Effects Nonnormality and Number of Longitudinal Data Series in Joint Modeling) Indahwati .; Aunuddin .; Khairil Anwar Notodiputro; I Gusti Putu Purnaba
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Joint modeling is intended to model longitudinal response process that affect the other primary response based on  assumption that both  processes induced by the same random effects. One of the assumptions that must be met in joint modeling is  normality  of  random  effects  and  intra-subject  error.  The  simulation  results show that the robustness of parameter estimates of joint model to the assumption of  random  effects  normality  can  be  achieved  by  increasing  the  frequency  of longitudinal observations.  Keywords:  longitudinal data,  joint modeling, robust
GENERALIZED VARIANCE FUNCTIONS FOR BINOMIAL VARIABLES IN STRATIFIED TWO-STAGE SAMPLING Ari Handayani; Aunuddin .; Indahwati .
FORUM STATISTIKA DAN KOMPUTASI Vol. 10 No. 1 (2005)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

      This empirical study evaluates the application of Generalized Variance Functions (GVFs) for binomial variables in the 1998 Indonesian Labor Force Survey. The survey employs stratified two-stage cluster sampling for selecting samples from a population of households. The study covers all provinces in Java to produce estimates at the level of Java Island. The relative variance estimates resulted from the GVF models are compared to the relative variance estimates which are computed directly. The results illustrate that  model  expressed by logarithmic model  log = log c + d log () gives a good approximation to estimate the variances for the nonagricultural employment group, especially for working male category both in urban and rural areas. It is also good for the total employment group differentiated by age group, educational attainment, and employment status. On the other hand, the model gives poor results for the agricultural employment group. Based on the empirical results, the GVF models may not perform particularly well for the common characteristics which have relatively dissimilar deff values to majority of characteristics in the same group, since these characteristics usually come out among all persons in the sample household and often among all households in the sample cluster as well. The success of the GVF technique depends critically on the grouping of the estimates total () and amount of characteristics involved as the observations for fitting the model. Furthermore, observations with relatively large residuals will also determine the performance of goodness-of-fit of the model. Application of GVF technique to obtain an approximate standard error on numerous binomial characteristics in large scale survey should be carried out further using extensive data. The better performance of GVF model may also be accomplished by utilizing, for examples, weighted least squares procedure or robust regression method. Additionally, the data users should be warned that there will inevitably be survey characteristics for which GVF's will give poor results or even no GVF will be appropriate. Keywords :  Generalized Variance Functions, Stratified Two-Stage Sampling
TINJAUAN EMPIRIK TERHADAP DUGAAN GALAT BAKU NILAI TENGAH YANG DIHASILKAN PROC SURVEYMEANS Bagus Sartono; . Indahwati; Wahyudi Setyo
FORUM STATISTIKA DAN KOMPUTASI Vol. 11 No. 1 (2006)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Penarikan contoh yang semakin kompleks berimplikasi pada proses perhitungan galat baku dugaan parameter semakin rumit. Kesulitan menentukan galat baku ini sering menyebabkan para analis dan peneliti menggunakan formula yang didasarkan pada teknik penarikan contoh acak sederhana. SAS menyediakan PROC SURVEYMEANS yang menghasilkan galat baku dengan formula yang disesuaikan dengan penarikan contohnya. Penelitian ini menunjukkan secara empiris bahwa galat baku dugaan parameter yang dihasilkan oleh PROC SURVEYMEANS memiliki tingkat akurasi yang baik. Indikasi ini ditunjukkan oleh selang kepercayaan yang tidak memuat nilai parameter sebenarnya mendekati tingkat kesalahan (a) yang digunakan.
PENDEKATAN KEKAR UNTUK MODEL BERSAMA (JOINT MODEL) ATAS DASAR SEBARAN t (A Robust Approach for Joint Model Based on t Distribution) _ Indahwati; _ Aunuddin; Khairil Anwar Notodiputro; I Gusti Putu Purnaba
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Existing methods for joint modeling are usually based on normality assumption of random effects and intra subject errors. We propose a joint model based on t distribution of the intra subject errors  to improve robustness of the estimation. Our model consists of two submodels: a mixed linear mixed effects model for the longitudinal data, and a generalized linear model for continuous/binary primary response. The proposed method is evaluated by means of simulation studies as well as application to HIV data. Keywords:  joint modeling, longitudinal data, robust, t distribution
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)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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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
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)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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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
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)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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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
SMALL AREA ESTIMATION FOR ESTIMATING THE NUMBER OF INFANT MORTALITY USING MIXED EFFECTS ZERO INFLATED POISSON MODEL Arie Anggreyani; _ Indahwati; Anang Kurnia
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Demographic and Health Survey Indonesia (DHSI) is a national designed survey to provide information regarding birth rate, mortality rate, family planning and health. DHSI was conducted by BPS in cooperation with National Population and Family Planning Institution (BKKBN), Indonesia Ministry of Health (KEMENKES) and USAID. Based on the publication of DHSI 2012, the infant mortality rate for a period of five years before survey conducted is 32 for 1000 birth lives. In this paper, Small Area Estimation (SAE) is used to estimate the number of infant mortality in districts of West Java. SAE is a special model of Generalized Linear Mixed Models (GLMM). In this case, the incidence of infant mortality is a Poisson distribution which has equdispersion assumption. The methods to handle overdispersion are binomial negative and quasi-likelihood model. Based on the analysis results, quasi-likelihood model is the best model to overcome overdispersion problem. However, after checking the residual assumptions, still resulted that residuals of model formed two normal distributions. So as to resolve the issue used Mixed Effect Zero Inflated Poisson (ZIP) Model. The basic model of the small area estimation used basic area level model. Mean square error (MSE) which based on bootstrap method is used to measure the accuracy of small area estimates.Keywords : SAE, GLMM, Mixed Effect ZIP Model, Bootstrap
Co-Authors A. A., Muftih Aditya Ramadhan Agus Mohamad Soleh Agustini , Ni Ketut Yulia Agustini, Ni Ketut Yulia Aji Hamim Wigena Akbar Rizki Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amelia, Reni Amin, Yudi Fathul Anang Kurnia Anik Djuraidah Antonius Benny Setyawan Ari Handayani Arie Anggreyani Aristawidya, Rafika Assyifa Lala Pratiwi Hamid Aunuddin . Bagus Sartono Budi Susetyo Cahyani Oktarina Chrisinta, Debora Daswati, Oktaviyani Dea Fisyahri Akhilah Putri Dian Kusumaningrum Erfiani Erfiani Erfiani Erfiani Erfiani Etis Sunandi Farit Mochamad Afendi Farit Mohamad Afendi Fatimah Fatimah Fira Nurahmah Al Aminy Fitrianto, Anwar Fulazzaky, Tahira Ghina Fauziah Hanifa Izzati Hari Wijayanto Harismahyanti A., Andi Hasanah, Lailatul I Gusti Putu Purnaba I Made Sumertajaya Iin Maena Indah, Yunna Mentari Irawan Irawan Jaya, Eddy Santosa Julianti, Elisa D Kamil, Farid Ikram Karunia, Nia Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kholidiah, Kholidiah Khusnia Nurul Khikmah Kusman Sadik Latifah, Leli Lestari, Nila Lili Puspita Rahayu Miranti, Ita Miranti, Ita Mohammad Masjkur Mualifah, Laily Nissa Mualifah, Laily Nissa Atul Muhammad Nur Aidi Naima Rakhsyanda Narindria, Yasmin Nadhiva Nurul Fadhilah Panjaitan, Intan Juliana Puput Cahya Ambarwati Putra, Stefanus Morgan Setyadi Perdana Putri, Christiana Anggraeni Ramdani, Indri Rasyid, Baharun Ray Sastri Regan, Regan Reni Amelia Reni Amelia Reza, Charolina Therezia Rifki Hamdani Rindy Anggun Pertiwi Salvina Salvina Silmi Annisa Rizki Manaf Siti Hafsah Siwi Haryu Pramesti Tahira Fulazzaky Tina Aris Perhati Titin Agustina Titin Suhartini Titin Suhartini, Titin Utami Dyah Syafitri Vera Maya Santi Vitona, Desi Wahyudi Setyo Yenni Angraini Yuniarty, Titin Zulkarnain, Rizky _ Aunuddin