Judul: Evaluasi Rintisan Sekolah Bertaraf Internasional Melalui Pendekatan Regresi Logistik Biner, Analisis Profil, dan Penskoran
Abstrak: Recorded since its independence, Indonesia has changed the education curriculum as much as eight times, starting from the Kurikulum Rencana Pembelajaran in 1947 to the Kurikulum Tingkat Satuan Pendidikan (KTSP) 2006. Recent curriculum change is accompanied by the implementation of a classification system based on the quality of schools into Sekolah Standar Nasional (SSN), Rintisan Sekolah Bertaraf Internasional (RSBI), and Sekolah Bertaraf Internasional (SBI). A school can be classified into SBI if it meets several criteria, one of which schools have received the recognition status of as RSBI from the related institutions. Ironically, from the initial implementation of the system, there is no school is managed to meet the criteria as an international school. This indicates that an error occurred in the education system. Based on the test results of binary logistic regression using backward elimination, obtained factors affecting international school’s status is location, ISO certification, the percentage of the poor students, school tuition, and the tuition per month. The result is supported by the value of the percentage of the overall classification accuracy was 85.7% and the value of Nagelkerke R2 of 0.647. Testing with profile analysis shows that there are no relationship parallels and similarities between RSBI and non RSBI schools in terms of school education expenses, which include school tuition and tuition per month. Nevertheless, through the comparison of the average cost of school education, it is known that the cost of education at the school RSBI almost two-fold higher than the non RSBI schools, it is consistent with the fact that occurs in society nowadays.
Keyword: binary logistic regression, international school, profile analysis, scoring

Judul: Perbandingan regresi logistik biner dengan jaringan syaraf tiruan (studi pada klasifikasi status akreditasi Sekolah Menengah Pertama Provinsi DKI Jakarta)
Abstrak: Classification is one of statistical technique in classifying data compiled systematically. One popular example of classification in the field of education is the accreditation status of the school. Accreditation status of schools affected by the standard of Teachers and Education Personnel (TOD). There are many statistical methods that can be used to solve classification problems, including binary logistic regression and Artificial Neural Network (ANN). The result of the calculations in studies using binary logistic regression gives an Area Under Curve (AUC) of 0.852 for training data and 0.834 for testing data. Calculation with a back propagation ANN for training data and testing data give an AUC of 0.911 and 0.899. Thus, the back propagation ANN can predict better than the binary logistic regression. The variables that significantly affect the classification of the accreditation status of the school is the number of teachers (X1) and the qualifications of chief administrative personnel (X6).
Keyword: Bogor Agricultural University (IPB), binary logistic regression., neural network, classification, back propagation

Judul: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
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