Analisa Perbandingan Metode Hierarchical Clustering, K-Means dan Gabungan Keduanya dalam Cluster Data (Studi Kasus: Problem Kerja Praktek Teknik Industri ITS)
Submission Date: 2012-08-01 09:52:38
Accepted Date: 2012-09-11 00:00:00
Abstract
Saat ini, konsep data mining semakin dikenal sebagai tools penting dalam manajemen informasi karena jumlah informasi yang semakin besar jumlahnya. Salah satu teknik yang dikenal dalam data mining adalah clustering, berupa proses pengelompokan sejumlah data atau objek ke dalam cluster (group) sehingga setiap dalam cluster tersebut akan berisi data yang semirip mungkin dan berbeda dengan objek dalam cluster yang lainnya. Clustering memiliki dua metode, yaitu partisi dan hierarki. Dua metode ini memiliki kelebihan dan kekurangan masing-masing, dan dengan menggabungkan keduanya dapat diperoleh hasil cluster yang lebih baik. Dari hasil cluster dengan menggunakan data problem Kerja Praktek Jurusan Teknik Industri ITS, maka diperoleh hasil bahwa gabungan metode Single Linkage Clustering dan K-means memberikan hasil cluster yang lebih baik dengan parameter uji cluster variance dan metode silhouette coefisien.
Keywords
Kerja Praktek; Document Clustering; K-means; Hierarchical Clustering; Cluster Variance; Metode Silhouette Coeficient
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