Judul: Geoinformatika pada Kasus Area Kecil dan Penerapannya untuk Mendeteksi Kantong-kantong Kemiskinan di Jember
Abstrak: Scan statistic is a method in Geinformatics considering spatial unsure to detect high or low potential area geographically and examine an area proportion parameter compare with others proportion statistically. This method detects extreme area (maximum or minimum) called hotspot. Hotspot means something unusual, anomalies, or critical condition area. Scan statistic is used to detect and evaluate area called Most Likely Cluster (MLC). It needs complete information about detected area with large sample size, while survey usually uses small sample size. One solution to overcome small sample size is by increasing sample size, but it is cost, time, and energy consuming. On the other side, there is a developing method in statistics to use survey data with small sample size, i.e. Small Area Estimation (SAE). Although this method is not used to compare among area, the biggest benefit from SAE is that it can be used to estimate area parameter complete, both surveyed and un-surveyed area. With complete estimation data, scan statistic can be used to detect hotspot or Most Likely Cluster (MLC). Based on the need of complete estimator value, SAE application is necessary in scan statistic. Quality of SAE application result in scan statistic depends on (i) sample size of hotspot, (ii) SAE estimation quality of model with some auxiliary variables and estimation methods, so SAE estimation methods need to be examined. SAE quality in handling small sample size case and scan statistic have been studied separately. This dissertation studies scan statistic in small sample size case, as an application of SAE on scan statistic. Estimation result gained through survey is called as Direct Estimate (DE), but usually with low accuracy and precision. There is a direct estimation proportion in MLC problem in Geoinformatics that can be replaced by estimation through SAE. This study’s purposes are to replace DE role with SAE estimation in Geoinformatics problem (in search for hotspot), to do cluster detection through scan statistic using small sample size by using SAE model into scan statistic, and use small area based Geoinformatics to detect poverty area pockets based on surveyed data and estimation of all village in Jember, East Java. This study is an application of SAE based scan statistic that is examined theoretically, by simulation, and by application on real data. SAE application on scan statistic can handle problems in detecting highly potential and statistically significant by using only small sample size. Simulation and application examination on real data are evaluated using statistic properties. Analysis continues with evaluation of correct classification on result gained. 100% correct classification through simulation examination on surveyed area is gained using Bayes and non-Bayes SAE. Bayes methods used in this study are spatial Hierarchical Bayes (HB) and non-spatial method, while non-Bayes methods used are EBLUP and SEBLUP. In un-surveyed area (large area), MLC will be bigger. HB1 (spatial correlation weighted) with hotspot proportion 0.95 is capable to estimate 80.97% correctly, while HB2 (nearest neighbor) is 75.62%. On the other side, EBLUP is only capable to estimate 65.99% of correct classification. From estimator property side, both methods (HB and EBLUP) have stable on result
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Judul: Spatial Bayesian Model in Small Area Estimation with Binary Response (Case: Estimation of Poor Families Proportion in Jember, East Java)
Abstrak: suatu subpopulasi dengan ukuran contoh kecil. Metode yang dikembangkan dalam pendugaan area kecil adalah metode pendugaan tidak langsung dengan memanfaatkan kekuatan area di sekitarnya dan sumber data di luar area. Tujuan dari metode pendugaan ini adalah untuk meningkatkan keefektifan ukuran contoh dan menurunkan keragaman dugaan parameter. Model-model dalam pendugaan area kecil mengasumsikan bahwa pengaruh acak galat area saling bebas. Namun dalam beberapa kasus, asumsi ini sering dilanggar. Pelanggaran asumsi ini disebabkan oleh keragaman suatu area dipengaruhi area sekitarnya, sehingga efek spasial dapat dimasukkan ke dalam pengaruh acak. Efek spasial merupakan hal yang lazim terjadi antara satu area dengan area yang lain, ini berarti bahwa area yang satu mempengaruhi area lainnya. Dalam statistika, model yang dapat menjelaskan hubungan antara suatu area dengan area sekitarnya adalah model spasial. Topik utama penelitian ini adalah membentuk model spasial Bayes pada pendugaan area kecil dengan model Logit-Normal yang akan diterapkan pada data kemiskinan. Tema yang diusung dalam penelitian ini yaitu pendugaan proporsi keluarga miskin di Kabupaten Jember Provinsi Jawa Timur. Penelitian ini akan menggunakan peubah respon yang menyebar menurut Binomial (Logit) dan pengaruh area yang menyebar menurut distribusi Normal. Hal ini yang mendasari pemakaian Model Logit-Normal. Tujuan utama yang ingin dicapai dalam penelitian ini, yaitu (1) Mengembangkan metode Bayes berhirarki khusus untuk data peubah respon biner dengan menambahkan pengaruh spasial, (2) mengkaji sifat statistik/penduga parameter proporsi berdasarkan metode Bayes berhirarki, dan (3) menerapkan metode pendugaan area kecil melalui pendekatan Bayes berhirarki untuk menentukan proporsi keluarga miskin di Kabupaten Jember Provinsi Jawa Timur. Studi kasus dilakukan pada dua sumber data, yaitu simulasi dan Susenas Kabupaten Jember Jawa Timur. Data simulasi digunakan untuk mengetahui berbagai karakteristik pendugaan pada beberapa pembobot spasial yang berbeda pada model Logit-Normal Bayes berhirarki berdasarkan pendugaan area kecil. Data kedua adalah data proporsi keluarga miskin untuk desa/kelurahan di Kabupaten Jember Jawa Timur. Data ini merupakan data Survei Sosial-Ekonomi Nasional (Susenas) 2008 yang berbasis rumah tangga serta data Potensi Desa (PODES) 2008 sebagai sumber data pendukung. Hasil simulasi dalam penelitian ini menunjukkan bahwa pada model Logit- Normal Bayes berhirarki dengan pembobot spasial tetangga terdekat dan pembobot spasial korelasi merupakan model terbaik bila dibandingkan dengan model Logit-Normal Bayes berhirarki dengan pembobot spasial jarak dan tanpa pembobot spasial. Hal ini ditunjukkan dengan nila rata-rata RMSE dan persentase bias relatif masing-masing model tersebut kecil., Small area estimation is a method to estimate parameters in a subpopulation with small sample size. The method is based on indirect estimation using the strength of the surrounding area and data sources outside the area to obtain statistic with adequate precision. Generally, the models in small area estimation assumes that the influence of random errors are independent area. However in somecases, this assumption is often violated. This is due to the variance of area which is affected by its neighborhood, so that spatial effect can be incorporated into random area effects. Rao (2003) stated that Logit-Normal model was one of the model in small area estimation which was affected by the spatial effect. It was used to estimate the proportion using Hierarchical Bayes (HB) method. The first objective of this research is to develope
Keyword: proportion of Hierarchical Bayes, the proportion of poor families, spatial weighted, Logit-Normal model, small area estimation

Judul: Embriogenesis ikan buta (Astyanax fasciatus)
Abstrak: Ikan buta Astyanax fasciatus adalah spesies ikan unik dari famili Characidae yang menempati perairan dalam gua di Wilayah La Cueva Chica-San Luis Potasi-Mexico. Ikan ini memiliki mata yang tereduksi (Blind Cavefish) dan warna kulit merah muda, pucat serta mengkilap.
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