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: Cluster Ensemble Analysis for Grouping Districts/Cities in Indonesia Based on Education Quality
Abstrak: Clustering merupakan metode yang cukup populer dan telah banyak digunakan di berbagai bidang. Pengelompokan dalam statistik dilakukan dengan menggunakan analisis gerombol. Penelitian ini bertujuan untuk mengelompokkan kabupaten/kota berdasarkan kualitas pendidikan dengan dua tipe data yang berbeda. Cluster ensemble adalah metode yang tepat untuk mengelompokkan data campuran. Metode ini menggabungkan hasil dari dua metode penggerombolan yang berbeda yang kemudian digabungkan untuk memperoleh hasil akhir. Metode untuk data numerik menggunakan metode agglomerative hierarchical clustering dan untuk data kategorik menggunakan algoritma squeezer. Pengelompokan 514 kabupaten/kota di Indonesia berdasarkan 18 peubah menghasilkan 3 gerombol. Jumlah anggota setiap gerombol adalah 446, 51, dan 17. Gerombol 1 merupakan wilayah dengan kualitas pendidikan terbaik berdasarkan indikator yang digunakan. Gerombol 2 dicirikan dengan rataan RLS dan HLS tertinggi serta rataan rasio murid terhadap guru yang paling besar dan sangat jauh dari batas ideal. Sementara itu, gerombol 3 menjadi gerombol dengan kualitas pendidikan yang paling rendah. Gerombol ini dicirikan dengan rataan RLS dan persentase guru terkualifikasi SMK yang sangat kecil., Clustering is a method that is quite popular and has been widely used in various fields. Clustering in statistics is done by using cluster analysis. This research aims to classify regions based on the quality of education with two different data types. Cluster ensemble are an appropriate method for clustering mixed data. This method combines the results of two different clustering methods which are then obtained into the final result. The method for numeric data uses the hierarchical clustering method and for categorical data uses the squeezer algorithm. Clustering of 514 district/cities in Indonesia based on 18 variables resulted in 3 clusters. The number of members of each cluster is 446, 51, and 17. Cluster 1 is the region with the best quality of education based on the indicators used. Cluster 2 is characterized by the highest average RLS and HLS and the highest student to teacher ratio which is very far from the ideal limit. Meanwhile, cluster 3 is the group with the lowest quality of education. This cluster is characterized by a very small average RLS and percentage of SMK qualified teachers.
Keyword: cluster ensemble, data campuran, pendidikan, squeezer

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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