Judul: Clustering the Quality of SHS/MA by Province Based on Accreditation Results Using the Fuzzy C-Means Method
Abstrak: Pemetaan mutu pendidikan di Indonesia perlu dikaji agar pemerintah provinsi sebagai lembaga yang bertanggung jawab atas kebijakan pengelolaan pendidikan menengah dapat lebih mudah dalam menentukan prioritas dan tindakan apa yang akan dilakukan guna meningkatkan kualitas pendidikan di Indonesia. Salah satu metode analisis yang dapat digunakan untuk memetakan mutu pendidikan adalah fuzzy c-means. Tujuan dari penelitian ini adalah menggerombolkan peta mutu provinsi di Indonesia berdasarkan hasil akreditasi SMA/MA dengan menggunakan metode fuzzy c-means. Metode fuzzy c-means dapat memperlihatkan peluang objek masuk ke dalam suatu gerombol dengan derajat keanggotaan. Ukuran gerombol optimum yang didapatkan adalah 2 dan 3. Solusi akhir dari hasil gerombol dengan ukuran gerombol 2 adalah 12 provinsi yang dikategorikan dalam gerombol 1 dan 22 provinsi yang dikategorikan dalam gerombol 2. Penggerombolan dengan jumlah gerombol 3 menghasilkan gerombol 1 yang beranggotakan 11 provinsi, gerombol 2 yang beranggotakan 16 provinsi, dan gerombol 3 yang beranggotakan 7 provinsi. Karakter utama dari gerombol 1 adalah skor standar nasional pendidikan yang tinggi, sedangkan karakter utama dari gerombol 2 adalah skor standar nasional pendidikan yang rendah. Kemudian karakter utama dari gerombol 3 adalah skor standar nasional yang nilainya berada di sekitar rata-rata nasional., Mapping the quality of education in Indonesia needs to be studied so that the provincial government, as the institution responsible for secondary education management policies, can more easily determine priorities and what actions will be taken to improve the quality of education in Indonesia. One of the analytical methods that can be used to map the quality of education is fuzzy c-means. This research aims to classify the quality maps of provinces in Indonesia based on the results of SHS/MA accreditation using the fuzzy c-means method. The fuzzy c means method can show the probability of objects entering a cluster with a degree of membership. The optimum cluster sizes obtained were 2 and 3. The final solution with cluster size 2 was 12 provinces categorized in cluster 1 and 22 provinces categorized in cluster 2. Clustering with cluster size 3 resulted in cluster 1 consisting of 11 provinces, cluster 2 consisting of 16 provinces, and cluster 3, which consists of 7 provinces. The main character of cluster 1 is a high national education standard score, while the main character of cluster 2 is a low national education standard score. Then the main character of group 3 is the national standard score, whose value is around the national average.
Keyword: fuzzy c-means, cluster analysis, validity index, degree of membership, euclidean distance

Judul: Penggerombolan Propinsi di Indonesia Berdasarkan Indikator Pendidikan Sekolah Lanjutan Atas Menggunakan Metode Ward dan Metode Fuzzy C-means
Abstrak: Education is one of factor success build a nation, not expection of Indonesia. Indonesia had done various education programs to create even distribution of education at all region in Indonesia. One of methods can use to knows this condition is cluster analysis. The purposes of this research are to clustering province in Indonesia building on indicator of senior high school education and to compare the result between Ward method and Fuzzy C-means. Data used in this research was secondary data indicator of senior high school education 2008, which obtained from Badan Pusat Statistik (BPS), Jakarta. Object used consist of 33 province in Indonesia. Indicator of senior high school education are: (1) Broken School Rate; (2) Ratio of Student and Class; (3) Ratio of Student and Teacher; (4) Ratio of Class and School; and (5) Participation Crude Rate. First step is used descriptive analysis to know the condition of senior high school education at each province. Cluster analysis use Ward method and Fuzzy C-means method. The reason they chosen are resulting a small variance at cluster and data used in this research is a few. Sum of optimum clustering at Fuzzy C-means is obtained from Index Xie Beni criteria. Before conducting cluster analysis, assumptions of correlation between variable must be checked. To evaluated it used Principal Componen Analysis. Transformation result of Principal Componen Analysis shows percentage variance cumulative reach 93% at fourth main component. The determining which the best method used different test mean vector use Lambda Wilks’ criteria and minimum objective function. As a result, characteristic between Ward method and Fuzzy Cmeans method is not different. Sum of cluster obtained at Ward method and Fuzzy C-means are 3 cluster. According to Lambda Wilks’ criteria and minimum objective function, Fuzzy C-means method better than Ward method for data of indicator of senior high school education.
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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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