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| Title | Klastering Varietas Padi Menggunakan Modifikasi Metode K-Means Berbasis OWA (Ordered Weighted Averaging) |
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| Author(s) | Millatul Ulya |
| Subject(s) | K-Means Clustering OWA Rice Varieties |
| Classification | 006 |
| Series Title | GMD | Tugas Akhir |
| Language | Indonesia |
| Publisher | Jurusan Teknik Industri FTI-ITS |
| Publishing Year | 2010 |
| Publishing Place | Surabaya |
| Collation | |
| Abstract/Notes | |
| Specific Detail Info | K-means clustering method based on Ordered Weighted Averaging (OWA) was developed by Cheng et al (2009) to resolve problem in classification using integrating k-means clustering and OWA. K-means clustering is a method of clustering and OWA is an aggregation operator. OWA was able to reduce the complexity of experimental data and help in representing sophisticated relationships between the criteria. Based on the original function of k-means and OWA algorithm used, it is predicted that OWA-based k-means clustering (Cheng et al, 2009) works by modifying some of its stages. In this study, it will be done by modification of OWA-based k-means clustering (Cheng et al, 2009) and applying it in the case of rice varieties clustering. This research aims to: (1) apply OWA-based k-means clustering in clustering data sets of rice varieties, and (2) Compare the silhouette value and the Sum of Squares Error (SSE) of Modified OWA-based k-means clustering with other clustering methods in clustering rice varieties. Result showed that the number of clusters that best match the data set of rice varieties was 7 clusters, based on the silhouette and the SSE value. Modified OWA-based k-means clustering (a = 0.8) compared to k-means clustering methods and hierarchical clustering was the best in clustering of rice varieties because it showed the smallest Sum of Squares Error. Keywords: k-means clustering, OWA, rice varieties. |
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| Pembimbing | Ir. Budi Santosa, M.Sc., PhD;Nani Kurniati, ST., MT |
| Volume | 1 |
| Availability | LOADING LIST... |
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