Pengelompokan Wilayah Madura Berdasar Indikator Pemerataan Pendidikan Menggunakan Partition Around Medoids Dan Validasi Adjusted Random Index
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Abstrak” Pemerataan pendidikan di Indonesia telah menjadi perhatian pemerintah sejak lama. Namun hingga saat ini, pendidikan di Indonesia masih belum merata. Hal tersebut dapat dilihat dari rendahnya nilai Angka Partisisipasi Kasar (APK) dan Angka Partisipasi Murni (APM) di daerah-daerah tertentu serta belum meratanya sarana dan prasarana pendidikan. Adapun tujuan penelitian ini adalah memberikan informasi kepada pemerintah setempat mengenai kondisi pendidikan di wilayahnya sehingga dapat menghasilkan kebijakan yang tepat mengenai pengembangan infrastuktur pendidikan dan distribusi guru bantu. Clustering adalah metode data mining yang membagi data kedalam kelompok yang mempunyai objek yang karakteristik sama. Penelitian ini menggunakan metode clustering Partition Around Medoids (PAM) dengan 3 distance measure: Manhattan, Euclidean dan Canberra distance. Untuk mengukur kualitas hasil clustering, digunakan nilai Adjusted Rand Index (ARI). Semakin besar nilai ARI, semakin baik kualitas cluster. Dari 3 kali ujicoba diperoleh rata-rata nilai ARI untuk Euclidean distance sebesar 0.799, Manhattan distance dengan rata-rata sebesar 0.738 dan Canberra distance sebesar 0.163. Sedangkan pengelompokan terbaik diperoleh menggunakan Euclidean distance dengan nilai ARI sebesar 0.825 dan kecocokan dengan label asli sebesar 83.33%. Dari pengelompokan terbaik menghasilkan kelompok pemerataan tinggi terdiri dari 11 kecamatan, kelompok pemerataan sedang terdiri dari 15 kecamatan dan kelompok pemerataan rendah terdiri dari 46 kecamatan.
Kata Kunci” indikator pemerataan pendidikan, clustering, Partition Around Medoid, distance measure, Adjusted Random Index
Abstract”Distribution of education in Indonesia has become government's attention for a long time. But until now, education in Indonesia is still not evenly distributed. This can be seen from the low value of Participation Rough figures and net enrollment ratio in certain areas as well as uneven educational facilities. The purpose of this research is to provide information to local authorities about the state of education in local region to produce an appropriate policy regarding development of educational infrastructure and teachers assistant distribution. Clustering is a data mining method that divides data into several groups with the same object characteristics. This research used Partition Around Medoids methods with 3 distance measure that contain Manhattan, Euclidean and Canberra distance. Adjusted Random Index used to measure the quality of clustering results. From 3 times sampling, better value of ARI Euclidean distance 0.799, Manhattan distance 0.738 and Canberra distance 0.163 while the best clustering obtained is Euclidean distance with value of ARI 0.825 and compatibility with the original label 83.33%. it is produces high equity group composed of 11 districts with equity groups are composed of 15 districts and low equity group consists of 46 sub-districts.
Keywords”Indicator of Educational Equity, Clustering, Partition Around Medoid, Distance Measure, Adjusted Random Index .
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