Journal of Information Systems Engineering and Business Intelligence
Abstrak
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 .
Hasil yang diperoleh dari penelitian ini dapat disimpulkan sebagai berikut:
Metode Partition Around Medoid (PAM) mampu diterapkan untuk memecahkan kasus pengelompokan wilayah / kecamatan berdasar indikator pemerataan pendidikan di 4 wilayah
kabupaten di Madura melalui pihak Dinas Pendidikan Kabupaten Bangkalan.
Pemilihan algoritma distance measure yang tepat untuk kasus pengelompokan memiliki pengaruh significant terhadap hasil clustering.
Dari 3 kali ujicoba diperoleh rata-rata nilai ARI untuk PAM menggunakan Euclidean distance sebesar 0.799, diikuti oleh Manhattan distance dengan rata-rata sebesar 0.738 dan rata-rata ARI
terendah dimiliki oleh PAM dengan Canberra distance sebesar 0.163.
Hasil analisa di atas menunjukkan bahwa kinerja PAM Manhattan dan PAM Euclidean lebih baik dibandingkan PAM Canberra.
Semakin besar nilai ARI maka semakin bagus kinerja suatu metode clustering.
Hasil Clustering adalah Dari 4 kabupaten dan 72 kecamatan di wilayah Madura, terdapat 11 kecamatan masuk ke dalam cluster kategori tinggi,
15 kecamatan masuk ke cluster sedang dan 46
PENGELOMPOKAN WILAYAH MADURA BERDASAR INDIKATOR PEMERATAAN PENDIDIKAN MENGGUNAKAN PARTITION AROUND MEDOIDS DAN VALIDASI ADJUSTED RANDOM INDEX .pdf .ppt
