Judul: Penerapan K-Medoids dalam Penggerombolan Provinsi di Indonesia berdasarkan Indikator Pendidikan Jenjang SMA
Abstrak: Program wajib belajar 12 tahun dicanangkan pemerintah pusat pada tahun 2012, tetapi di Indonesia masih terdapat ketimpangan pendidikan khususnya jenjang SMA. Untuk mengatasi hal tersebut, penting mengetahui daerah-daerah mana yang harus ditingkatkan pendidikannya. Penelitian ini bertujuan untuk menggerombolkan provinsi di Indonesia berdasarkan indikator pendidikan jenjang SMA serta mendapatkan deskripsi dari masing-masing gerombol yang terbentuk Metode yang digunakan pada penelitian ini adalah analisis gerombol. Oleh karena terdapatnya pencilan pada data, maka metode analisis gerombol yang tepat digunakan adalah K-Medoids. Hasil yang diperoleh dari penelitian ini adalah terbentuknya empat gerombol. Gerombol 1 terdiri dari tiga provinsi yang memiliki karakteristik tingginya persentase putus sekolah, rasio murid-guru, dan persentase ruang kelas kondisi baik, serta rendahnya persentase mengulang. Gerombol 2 beranggotakan 21 provinsi memiliki karakteristik tingginya persentase lulusan, rasio murid-sekolah, dan rasio murid-kelas sangat tinggi, serta rendahnya persentase putus sekolah. Gerombol 3 beranggotakan delapan provinsi memiliki karakteristik tingginya persentase guru layak, dan rendahnya rasio murid-guru, serta rasio murid-kelas. Sementara itu, Gerombol 4 beranggotakan dua provinsi memiliki karaktetistik tingginya persentase mengulang dan rendahnya persentase lulusan, rasio murid-sekolah, persentase guru layak, serta persentase kelas kondisi baik. Daerah yang perlu lebih diperhatikan ada pada Gerombol 4 karena memiliki beberapa aspek pendidikan yang kurang baik., In 2012, the 12-year compulsory education program was launched by Indonesian government. But there is still educational inequality, especially at senior high school. To resolve this problem, it is important to know which areas should be improved in terms of education. This study clusters provinces in Indonesia based on senior high school education indicators and obtain descriptions of each cluster. The method used in this study is cluster analysis. Due to the presence of outliers, the clustering method K-Medoids is used. The result of this study is four clusters are formed. Cluster 1 has three provinces which are characterized by high percentage of dropout, student-teacher ratio, and percentage of classrooms with good condition, as well as low percentage of repetition. Cluster 2 has 21 provinces which are characterized by high percentage of graduate, student-school ratio, and student-classroom ratio, as well as low percentage of dropout. Cluster 3 has eight provinces which are characterized by high percentage of eligible teachers, as well as low student-teacher ratio and student-classroom ratio. Meanwhile Cluster 4 has two provinces which are characterized by high percentage of repetition, as well as low percentage of graduate, student-school ratio, percentage of eligible teachers, and percentage of classrooms with good condition. Areas in Cluster 4 should be paid more attention because they have some educational aspects that are not good enough.
Keyword: cluster analysis, education, k-medoids, high school

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: Pengaruh ekstrak tempuyung (Sonchus arvensis) terhadap aktivitas xantin oksidase secara in vitro sebagai dasar uji kinetika
Abstrak: Sonchus arvensis is one of traditional medicinal plants that has a potency as antigout but its inhibition mechanism is not known yet. This research was done as a basic means to test inhibition kinetics. The test result curve was presented in the Michaelis Menten plot. The result showed that the inhibition percent of S. arvensis crude extract in inhibiting xanthine oxidase activity was 1.61-10.86 (100- 600 ppm). The addition of substrate concentration increased enzymatic reaction velocity. The S. arvensis crude extract with concentration at 200 ppm (percentage of inhibition at 10.86) was proven able to reduce enzymatic reaction but not in significant level.
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