Judul: Hubungan Kekurangan Iodium dengan Prestasi Belajar Murid Sekolah Dasar (SD) di Propinsi Nusa Tenggara Timur
Abstrak: Tujuan penelitian ini adal ah mengetahui hubungan kekurangan iodium dengan prestasi belajar murid Sekolah Dasar di daerah gondok endemik kabupaten Timor Tengah Selatan prapinsi Nusa Tenggara Timur. Penelitian ini merupakan pengolahan data sekunder hasil survei evaluasi dampak program penanggulangan gangguan akibat kekurangan iodium (GAKI) d i Propinsi Nusa Tenggara Timur.
Keyword: 

Judul: Beberapa faktor yang mempengaruhi defisiensi iodium dan kaitannya dengan prestasi belajar anak Sekolah Dasar : studi kasus pada keluarga yang tinggal di Daerah Gondok Endemik di Kecamatan Kaliwiro dan Leksono, Wonosobo, awa Tengah
Abstrak: Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang berpengaruh terhadap defisiensi Iodium anak SD serta bagaimana keterkaitannya dengan keberhasilan pendidikan anak sekolah dasar pada keluarga yang tinggal di daerah gondok endemik. Penelitian dilakukan di Desa Medono dan Tanjunganom yang termasuk wilayah Kecamatan Kaliwiro serta Desa Besani dan Kalimendong yang termasuk wilayah Kecamatan Leksono, Kabupaten Wonosobo, Jawa Tengah. Data penelitian dikumpulkan pada bulan Juli sampai bulan Agustus 1994, terdiri dari data primer dan sekunder. Data primer diperoleh melalui wawancara dengan alat bantu kuesioner yang meliputi : identitas contoh dan keluarganya, pendapatan per kapita perbulan, pendidikan formal orang tua, pengetahuan, sikap dan praktek yang berhubungan dengan GAKI pada orang tua, data berat dan tinggi badan contoh yang diperoleh melalui pengukuran serta data konsumsi pangan keluarga yang dikumpulkan dengan cara recall. Data sekunder meliputi kadar ekskresi Iodium dalam urine (UEI) yang diperoleh dari Laboratorium GAKI Undip dan RS dr. Suharso, Semarang serta data prestasi belajar contoh yang diambil dari nilai rapor 3 catur wulan terakhir, dan data keadaan umum wilayah penelitian yang diperoleh dari administrasi pemerintah setempat. Data kemudian dianalisis secara deskriptif dan statistik menggunakan uji korelasi Spearman, uji sidik ragam dan uji regresi linear berganda.
Keyword: Iodium

Judul: Development of Face Recognition Model using Bi-2DPCA and Support Vector Machine
Abstrak: The human face is always used in the application of access control systems, identity authentication systems, surveillance systems and security systems. Some researh on face recognition has been proposed with a variety of methods, such as Eigen Faces, Fisher’s Linear Discriminant, Bayesian Networks, Neural Networks,Hidden Markov Model, and so on. But all the above mentioned methods have limitations, such as dealing with the size of the image and lighting and learning time are too long. In a study to obtain an alternative method for face recognition, Le TH and Bui L (2011) conducted research model of face recognition with Two- DimensionalPrincipal Component Analysis (2DPCA) and Support Vector Machine (SVM) using AT&T face image data. The results of these studies that the accuracy of face recognition use 2DPCA and SVM was 97.3% and PCA-SVM was 95.2%. Then in 2010, Bi-2DPCA uses as alternative method of face recognition performed by Yang J et al. (2010). In a study comparing methods of Bi-2DPCA, 2DPCA and PCA. The conclusion that the use of Bi-2DPCA generating and computational accuracy was better than 2DPCA and PCA Based on research conducted by Le TH and Bui L (2011) and Yang J et al. (2010), conducted research for the development of models of face recognition using dimension reduction technique Bi-2DPCA and Support Vector Machine (SVM) as pattern recognition. 1DPCA and 2DPCA dimension reduction technique are used as comparison measure dimensions and computing time than using Bi-2DPCA technique. To help SVM in recognizing human faces, SVM use linear, polynomial and radial basis function (RBF) kernel function. When using RBF kernel, used for the RBF kernel, the parameter for sigma is 2-15 up to 23 and the value of C starting from 2-5 up to 214 which have been used in a study conducted by Le TH and Bui L (2011). Facial image data used are taken from the ORL Face Database AT&T Laboratory, which contain 40 faces, each face with 10 images, and total of image 400 pieces. Validation is done by Leave-One-Out (LOO) cross validation, so that for each pair of extraction and classifier method is 10 trials. One against all method used to help SVM in classifying the ORL face data consists of 40 classes, to reduce the computational time. From the results of tests on 36 models of face recognition , Bi-2DPCA85% with the help of a linear kernel generates the highest level of face recognition accuracy of 94.25 %, and the lowest accuracy rate of 24.50 % was obtained with 1DPCA-kernel polynomial ordo 3 . While the lowest computational time of 15.34 seconds when component is used by 85 % computation time and a maximum of 252.68 seconds when component is 95 %. There are several things that could be concluded that: pattern accuracy on each kernel feature extraction are the same. This means that there is no relationship between the feature extraction used by the kernel to the SVM. The second is, in this study SVM cannot classify large dimension data. From the results of tests on 36 models of face recognition, Bi2DPCA85% with the help
Keyword: Face Recognition, Principle Component Analysis, Kernel, Support Vector Machine, Leave-one Out Cross Validation