Judul: Pengkajian Pembentukan Model Klasifikasi dalam Pengelompokkan Jurusan Siswa di SMA (Studi Kasus: Siswa SMA Negeri Siau Timur Kabupaten Siau Tagulandang Biaro Propinsi Sulawesi Utara)
Abstrak: Modeling that involve categorical response variables give important role in the classification problem. Statistical analysis is applied to solve this problem are discriminant analysis and multinomial logistic regression. Implementation of both methods against student of senior high school of East Siau data produce multinomial logistic regression as best method for classify the students into Scicence Program, Social Program, and Language Program. Classification accuracy of model from resampling is 88.1% and of model validation from Tagulandang Senior High School is 70.6%. The variables give significantly effect in classification students to Science Program or Language Program are Mathematics, English, Chemistry, and German, whereas, classification students into Social Program or Language Program are Economy, English, German, and History.
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Judul: Model Regresi Logistik Biner untuk Identifikasi Faktor-Faktor yang Mempengaruhi Siswa Masuk Jurusan IPA/IPS
Abstrak: One direction is desired for students and parents is the science classes. On the one hand, this course allows the student has a choice of majors in universities more than other majors and a lot of work to only accept students from science classes. Ironically, many students who choose to major in science but does not support the student's ability. Dealing with the issue, it must be examined factors that influence students major in science. One method used to analyze the factors that affect students entering the science classes of binary logistic regression. Response variables used in this study categorical form of data on the status of the incoming students are students majoring in science or social studies. The data used in this study is population data class XI of SMAN 2 Bogor, West Java, the academic year 2012/2013. The number of total population is 279 students. Results of analysis of this study is binary logistic regression models produce values overall classification accuracy of 92.8% for the model classification table and 92.7% for the ROC curve. Explanatory variables used in this study a total of 18 explanatory variables and the explanatory variables are eight ties associated with variable responses to the real level of 5%. Wald test results obtained by the three explanatory variables that significantly influence the status of the students choose a major motivation, student aspirations and students are happy with the lesson or not exact.
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Judul: Infeksi Virus Parainfluenza Pada Anjing
Abstrak: Virus parainfluenza anjing termasuk famili Paramyxoviridae, berbentuk pleomorf dengan ukuran bervariasi (bergaris tengah antara 100-500 nm), berpembungkus peka ether, berisi genom RNA dan secara serologis mempunyai kesamaan dengan virus SV-5 kera; dikenak sebagai salah satu kausa penting dari sindrom "kennel cough". Infeksinya dapat mengenai anjing semua umur yang belum pernah terinfeksi atau mendapatkan vaksinasi mengakibatkan gejala klinis ringan yang terbatas pada traktus respirasi atu infeksi yang secara klinis tidak memperlihatkan gejala nyata. Gejala klinis yang nampak setelah masa inkubasi (1-5 hari) terlewati berupa batuk yang tidak berat selama beberapa hari, berbagai tingkat tonsilitis dan pharyngitis dan kadang-kadang ditemukan kenaikan temperatur rektal antara 1-2 derajat C dari temperatur normal rat-rata. Anjing terinfeksi mengandung virus pada traktus respirasi bagian atas dan bawah serta limfonoduli sampai hari ke 9 setelah infeksi. Penyebaran virus antar anjing terjadi secara aerogen.
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