PENGOPTIMALAN PENGUKURAN BREGMAN DIVERGENCES MENGGUNAKAN DAVIES BOULDIN INDEX
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Abstract
Penentuan cluster optimal ini seringkali ambigu karena dihasilkan dari beberapa kelompok. Pemilihan informasi dari kelompok mana yang akan digunakan oleh pengguna menjadi masalah tersendiri karena menyangkut pembuatan kebijakan. Davies Bouldin Index (DBI) merupakan teknik evaluasi cluster untuk menentukan jumlah cluster yang optimal dan didukung dengan pengukuran jarak yang tepat. Penelitian ini bertujuan untuk mendapatkan cluster melalui teknik DBI yang diterapkan pada pengukuran Bregman Divergences, Mahalano dan Square Euclidean Distance, menggunakan algoritma K-Means dan K-Medoids. Hasil pengujian ditunjukkan melalui beberapa indikator yang digunakan sebagai tolak ukur untuk mengetahui kinerja Bregman Divergences dalam menentukan jumlah cluster seperti korelasi, algoritma cluster yang digunakan, pola DBI, hasil DBI, k-optimal dan waktu yang dibutuhkan untuk pengujian. Melalui kedua algoritma clustering tersebut, jarak Mahalano dapat menghasilkan pola pengelompokan yang konsisten dan teknik pengukuran Square Euclidean Distance berhasil menunjukkan performa DBI terbaik yang menempatkan k=2 sebagai cluster optimal, nilai DBI terendah sebesar 0,882 dan 1,030 pada waktu pengujian. selama 0 detik.
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