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<![CDATA[PROPORSI KEMISKINAN DI KABUPATEN BOGOR ]]>
<![CDATA[Suhartini, Titin]]>;
<![CDATA[Sadik, Kusman]]>;
<![CDATA[Indahwati, Indahwati]]>
<![CDATA[ Sosio Informa Vol 1, No 2 (2015): Sosio Informa ]]>
Publisher : <![CDATA[Puslitbangkesos ]]>
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<![CDATA[Kemiskinan merupakan salah satu permasalahan mendasar yang menjadi pusat perhatian pemerintahIndonesia. Aspek penting untuk mendukung strategi penanggulangan kemiskinan adalah ketersediaandata dan informasi yang akurat. Penelitian ini bertujuan untuk menduga proporsi status kemiskinan rumahtangga pada tingkat kecamatan di Kabupaten Bogor dan mengidentifikasi sumber/jenis pekerjaan rumahtangga. Metode yang disusun berdasarkan pendugaan langsung dengan asumsi metode sampel acaksederhana untuk memperoleh penduga proporsi dan berdasarkan tabulasi silang untuk mengetahui latarbelakang jenis pekerjaan yang berdampak pada kemiskinan. Penelitian ini menggunakan data sekunderberupa Survei Sosial Ekonomi Nasional (Susenas) dengan variabel terpilih. Badan Pusat Statistik memilikiprogram pengumpulan data melalui sensus dan survei. Survei tersebut menggunakan metode rancanganpenarikan sampel yang kompleks. Hasil penelitian menunjukkan bahwa rumah tangga miskin di KabupatenProporsi Kemiskinan di Kabupaten Bogor, Titin Suhartini, Kusman Sadik, dan Indahwati 161Bogor sebesar 6,84%. 31,08% rumah tangga miskin berasal dari jenis pekerjaan pertanian tanaman pangan.Hanya 24 kecamatan yang dapat dilakukan pendugaan proporsi status kemiskinan rumah tangga.Pendugaanproporsi rumah tangga miskin terbesar berada di kecamatan Nanggung yaitu sebesar 45%. Untuk mengatasiketerbatasan pendugaan yang dilakukan terhadap 16 kecamatan lainnya dapat menggunakan alternatifmetode pendugaan area kecil.Kata Kunci: pendugaan, proporsi, rumah tangga.]]>
<![CDATA[Pemodelan Pengukuran Luas Panen Padi Nasional Menggunakan Generalized Autoregressive Conditional Heteroscedastic Model (GARCH) ]]>
<![CDATA[Iqbal, Teuku Achmad]]>;
<![CDATA[Sadik, Kusman]]>;
<![CDATA[Sumertajaya, I Made]]>
<![CDATA[ Jurnal Penelitian Pertanian Tanaman Pangan Vol 33, No 1 (2014): April 2014 ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengembangan Tanaman Pangan ]]>
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DOI: 10.21082/jpptp.v33n1.2014.p17-26
<![CDATA[This study was aimed to build a model for the estimation of national harvested area of rice by incorporating element of variant heterogeneity and the influence of asymmetry factors on time series data using five types of GARCH models, namely: symmetric GARCH, exponential asymmetric GARCH, quadratic asymmetric GARCH, Threshold GARCH, and non-linear asymmetric GARCH. Those models were compared and evaluated, and then the best model was used to predict the accuracy of the national rice harvested area. The results showed that two types of GARCH had significant coefficient, indicating the validity of the model. Those models were symmetric GARCH and quadratic GARCH models. Based on the value of mean absolute percentage error (MAPE) for the twelve month periods ahead, quadratic GARCH model was better than the symmetric GARCH model. Furthermore, based on the value of mean absolute deviation (MAD) and mean square error (MSE), quadratic GARCH model also seemed to be a better model than symmetric GARCH model. The best model can be used to predict the harvested area in the subsequent year.]]>
<![CDATA[METODE E-BLUP DALAM SMALL AREA ESTIMATION UNTUK MODEL YANG MENGANDUNG RANDOM WALK ]]>
<![CDATA[Kusman . Sadik]]>;
<![CDATA[Khairil Anwar Notodiputro]]>
<![CDATA[ FORUM STATISTIKA DAN KOMPUTASI Vol. 11 No. 2 (2006) ]]>
Publisher : <![CDATA[FORUM STATISTIKA DAN KOMPUTASI ]]>
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<![CDATA[Ada dua topik utama yang menjadi perhatian para statistisi dalam membahas persoalan survei. Yaitu persoalan pengembangan teknik penarikan contoh (sampling technique) dan pengembangan metodologi pendugaan parameter pupulasi (estimation methods). Adapaun persoalan mutakhir dalam metodologi pendugaan adalah menyangkut pendugaan untuk daerah atau domain survei yang memiliki contoh kecil atau bahkan tidak memiliki contoh satupun, Rao(2003). Misalnya survei untuk unit rumah tangga pada suatu survei berskala nasional. Umumnya untuk survei demikian banyaknya contoh rumah tangga untuk tiap kabupaten dalam suatu propinsi sangat kecil (small area). Bahkan bisa terjadi kabupaten tertentu tidak terpilih sebagai daerah survei sehingga contoh rumah tangga dari kabupaten tersebut tidak ada. Metode pendugaan langsung (direct estimation) untuk daerah atau kabupaten yang bersangkutan menjadi tidak layak karena contohnya terlalu kecil. Pada makalah ini akan dipaparkan metode pendugaan daerah kecil (small area estimation) dengan pendekatan pendugaan tidak langsung berbasis model (indirect estimation - model based). Khususnya untuk model yang mengandung langkah acak (random walk). Kata Kunci : direct estimation, indirect estimation, generalized regression, general linear mixed model, empirical best linear unbiased prediction, block diagonal covariance, random walk.]]>
<![CDATA[Parameter Quantile-like dalam Pendugaan Area Kecil Melalui Pendekatan Penalized- Splines ]]>
<![CDATA[Kusman Sadik]]>
<![CDATA[ STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 8, No 1 (2008) ]]>
Publisher : <![CDATA[Program Studi Statistika Unisba ]]>
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DOI: 10.29313/jstat.v8i1.972
<![CDATA[Pada beberapa tahun terakhir ini, para statistisi mulai mengembangkan metodologi yang berkaitandengan pendugaan untuk daerah atau domain survei yang memiliki sampel kecil atau bahkan tidakmemiliki sampel satupun. Data yang diperoleh melalui teknik survei yang tepat akan sangat efektifdan memiliki sifat reliabilitas untuk menduga total atau rataan peubah tertentu. Sifat penduga yangdemikian dapat dicapai apabila data sampel dari survei mencakup daerah atau domain yang besar.Misalnya, beberapa survei ekonomi yang dilakukan di Indonesia berskala nasional. Pada survei yangdemikian banyaknya sampel rumah tangga untuk tiap kecamatan dalam suatu kabupaten sangatkecil (small area). Bahkan bisa terjadi suatu kecamatan tertentu tidak terpilih sebagai daerah surveisehingga sampel rumah tangga dari kecamatan tersebut tidak ada. Persoalannya adalah bagaimanamenduga parameter, misalnya tingkat kemiskinan di level kecamatan tersebut sementara sampelnyasangat kecil. Salah satu metode yang banyak dikembangkan untuk pendugaan area kecil (small areaestimation / SAE) adalah model yang berbasis pada generalized linear mixed model (GLMM).Beberapa pendekatan lain saat ini mulai didiskusikan oleh para statistisi di dunia. Salah satumetode alternatif tersebut adalah pemodelan yang didasarkan pada kuantil yang dikenal dengan MquantileP-splines. Aspek penting dari metode ini adalah adanya sifat tegar (robust) terhadappencilan (outliers) dan bebas sebaran (distribution free).]]>
<![CDATA[KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL ]]>
<![CDATA[Khusnul Khotimah]]>;
<![CDATA[Kusman Sadik]]>;
<![CDATA[Akbar Rizki]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i1.502
<![CDATA[Regression is a statistical method that is used to obtain a pattern of relations between two or more variables presented in the regression line equation. This line equation is derived from estimation using ordinary least squares (OLS). However, OLS has limitations that are highly dependent on outliers data. One solution to the outliers problem in regression analysis is to use the robust regression method. This study used the least median squares (LMS) and multi-stage method (MM) robust regression for analysis of data containing outliers. Data analysis was carried out on generation data simulation and actual data. The simulation results of regression analysis in various scenarios are concluded that the LMS and MM methods have better performance compared to the OLS on data containing outliers. MM method has the lowest average parameter estimation bias, followed by the LMS, then OLS. The LMS has the smallest average root mean squares error (RMSE) and the highest average R2 is followed by the MM then the OLS. The results of the regression analysis comparison of the three methods on Indonesian rice production data in 2017 which contains 10% outliers were concluded that the LMS is the best method. The LMS produces the smallest RMSE of 4.44 and the highest R2 that is 98%. MM's method is in the second-best position with RMSE of 6.78 and R2 of 96%. OLS method produces the largest RMSE and lowest R2 that is 23.15 and 58% respectively.]]>
<![CDATA[A SIMULATION STUDY OF LOGARITHMIC TRANSFORMATION MODEL IN SPATIAL E MPIRICAL BEST LINEAR UNBIASED PREDICTION (SEBLUP) METHOD OF SMALL AREA ESTIMATION ]]>
<![CDATA[Hazan Azhari Zainuddin]]>;
<![CDATA[Khairil Anwar Notodiputro]]>;
<![CDATA[Kusman Sadik]]>
<![CDATA[ FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015) ]]>
Publisher : <![CDATA[FORUM STATISTIKA DAN KOMPUTASI ]]>
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<![CDATA[There have been many studies developed to improve the quality of estimates in small area estimation (SAE). The standard method known as EBLUP (Empirical Unbiased Best Linear Predictor) has been developed by incorporating spatial effects into the model. This modification of the method was known SEBLUP (Spatial EBLUP) since it incorporates the spatial correlations which exist among the small areas. The data obtained (variables of concern) usually have a large variance and tend to have a a nonsymmetric distribution and therefore tend to have nonlinear relationship pattern between concomitant variables and variables of concern. the results showed that the method SEBLUP using logarithmic transformation produces estimator more than the other methods.Keywords : EBLUP, SAE, SEBLUP]]>
<![CDATA[There have been two main topics developed by statisticians in a survey, i.e. sampling techniques and estimation methods. The current issues in estimation methods related to estimation of a particular domain having small size of samples or, in more extreme cases, there is no sample available for direct estimation. Sample survey data provide effective reliable estimators of totals and means for large area and domains. But it is recognized that the usual direct survey estimator performing statistic ]]>
<![CDATA[Kusman Sadik]]>
<![CDATA[ FORUM STATISTIKA DAN KOMPUTASI Vol. 14 No. 2 (2009) ]]>
Publisher : <![CDATA[FORUM STATISTIKA DAN KOMPUTASI ]]>
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<![CDATA[There have been two main topics developed by statisticians in a survey, i.e. sampling techniques and estimation methods. The current issues in estimation methods related to estimation of a particular domain having small size of samples or, in more extreme cases, there is no sample available for direct estimation. Sample survey data provide effective reliable estimators of totals and means for large area and domains. But it is recognized that the usual direct survey estimator performing statistics for a small area, have unacceptably large standard errors, due to the circumstance of small sample size in the area. The most commonly used models for this case, usually in small area estimation, are based on generalized linear mixed models. Some time happened that some surveys are carried out periodically so that the estimation could be improved by incorporating both the area and time random effects. In this paper we propose a state space model which accounts for the two random effects and is based on two equation, namely transition equation and measurement equation. Based on a evaluation criterion, the proposed hierarchical Bayes estimator turns out to be superior to both estimated best linear unbiased prediction (BLUP) and the direct survey estimator. The posterior variances which measure accuracy of the hierarchical Bayes estimates are always smaller than the corresponding variances of the BLUP and the direct survey estimates.]]>
<![CDATA[Empirical Bayesian Method for the Estimation of Literacy Rate at Sub-district Level Case Study: Sumenep District of East Java Province ]]>
<![CDATA[A.Tuti Rumiati]]>;
<![CDATA[Khairil Anwar Notodiputro]]>;
<![CDATA[Kusman Sadik]]>;
<![CDATA[I Wayan Mangku]]>
<![CDATA[ IPTEK The Journal for Technology and Science Vol 23, No 1 (2012) ]]>
Publisher : <![CDATA[IPTEK, LPPM, Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.12962/j20882033.v23i1.13
<![CDATA[This paper discusses Bayesian Method of Small Area Estimation (SAE) based on Binomial response variable. SAE method being developed to estimate parameter in small area due to insufficiency of sample. The case study is literacy rate estimation at sub-district level in Sumenep district, East Java Province. Literacy rate is measured by proportion of people who are able to read and write, from the population of 10 year-old or more. In the case study we used Social Economic Survey (Susenas)data collected by BPS. The SAE approach was applied since the Susenas data is not representative enough to estimate the parameters at sub-district level because it’s designed to estimate parameters in regional area (in scope of a district/city at minimum). In this research, the response variable being used was logit function trasformation of pi (the parameter of Binomial distribution). We applied direct and indirect approach for parameter estimation, both using Empirical Bayes approach. For direct estimation we used prior distribution of Beta distribution and Normal prior distribution for logit function (pi) and to estimate parameter by using numerical method, i.e integration Monte Carlo. For indirect approach, we used auxiliary variables which are combinations of sex and age (which is divided into five categories). Penalized Quasi Likelihood (PQL) was used to get parameter estimation of SAE model and Restricted Maximum Likelihood method (REML) for MSE estimation. Instead of Bayesian approach, we are also conducting direct estimation using classical approach in order to evaluate the quality of the estimators. This research gives some findings, those are: Bayesian approach for SAE model gives the best estimation because having the lowest MSE value compares to the other methods. For the direct estimation, Bayesian approach using Beta and logit Normal prior distribution give a very similar result to the direct estimation with classical approach since the weight of is too large, which is about 0.905. It is also found that direct estimation using Bayesian approach with the Beta prior distribution gives better MSE than using logit normal prior distribution.]]>
<![CDATA[Simulation Study of Robust Geographically Weighted Empirical Best Linear Unbiased Predictor on Small Area Estimation: Simulasi Metode Prediksi Tak Bias Linier Terbaik Empiris Terboboti Geografis Kekar pada Pendugaan Area Kecil ]]>
<![CDATA[Naima Rakhsyanda]]>;
<![CDATA[Kusman Sadik]]>;
<![CDATA[Indahwati Indahwati]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v5i1p50-60
<![CDATA[Small area estimation can be used to predict the population parameter with small sample sizes. For some cases, the population units that are close spatially may be more related than units that are further apart. The use of spatial information like geographic coordinates are studied in this research. Outlier contaminations can affect small area estimations. This study was conducted using simulation methods on generated data with six scenarios. The scenarios are the combination of spatial effects (spatial stationary and spatial non-stationary) with outlier contamination (no outlier, symmetric outliers, and non-symmetric outliers). The purpose of this study was to compare the geographically weighted empirical best linear unbiased predictor (GWEBLUP) and robust GWEBLUP (RGWEBLUP) with direct estimator, EBLUP, and REBLUP using simulation data. The performance of the predictors is evaluated using relative root mean squared error (RRMSE). The simulation results showed that geographically weighted predictors have the smallest RRMSE values for scenarios with spatial non-stationary, therefore offer a better prediction. For scenarios with outliers, robust predictors with smaller RRMSE values offer more efficiency than non-robust predictors.]]>
<![CDATA[A COMPARISON OF POLYTOMOUS MODEL WITH PROPORTIONAL ODDS AND NON-PROPORTIONAL ODDS MODEL ON BIRTH SIZE CASE IN INDONESIA ]]>
<![CDATA[Kurniawati, Yenni]]>;
<![CDATA[Kurnia, Anang]]>;
<![CDATA[Sadik, Kusman]]>
<![CDATA[ MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.14.1.79-88
<![CDATA[The proportional odds model (POM) and the non-proportional odds model (NPOM) are very useful in ordinal modeling. However, the proportional odds assumption is often violated in practice. In this paper, the non-proportional odds model is chosen as an alternative model when the proportional odds assumption is not violated. This paper aims to compare Proportional Odds Model (POM) and Non-Proportional Odds Model (NPOM) in cases of birth size in Indonesia based on the 2017 Indonesian Demographic and Health Survey (IDHS) data. The results showed that in the POM there was a violation of the proportional odds assumption, so the alternative NPOM model was used. NPOM had better use than POM. The goodness of fit shows that the deviance test failed to reject H0, and the value of Mac Fadden R2 is higher than POM. The risk factors that have a significant influence on all categories of birth size are the residence and gender of the child.]]>
