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Abstrak :
Indeks validitas merupakan metode yang mengevaluasi hasil clustering untuk mendapatkan jumlah klaster optimal suatu data. Pada skripsi ini, dilakukan clustering pada data menggunakan algoritma K-Means. Selanjutnya, hasil clustering tersebut dievaluasi oleh empat jenis indeks validitas, yaitu indeks Silhouette, indeks Davies-Bouldin, indeks Sum of Square, dan indeks Calinski-Harabasz. Implementasi keempat jenis indeks validitas dilakukan dengan menggunakan data benchmark yang sudah diketahui jumlah kelasnya. Hasil implementasi tersebut akan dibandingkan untuk mengetahui apakah keempat indeks validitas dapat memprediksi jumlah klaster dengan tepat. Dari hasil simulasi, indeks Silhouette, indeks Davies-Bouldin, dan indeks Calinski-Harabasz dapat memprediksi jumlah klaster optimal lebih tepat dibandingkan dengan indeks Sum of Square. ......The validity index is a method that evaluates the clustering results to get the optimal number of clusters of a data. In this skripsi, data clustered using K Means algorithm. Furthermore, the clustering results are evaluated by four types of validity indices, namely the Silhouette index, the Davies Bouldin index, the Sum of Square index, and the Calinski Harabasz index. The implementation of the four validity indices is done by using the benchmark data which is already known to the number of classes. The results of the implementation will be compared to find out whether the four validity indices can predict the number of clusters appropriately. From the simulation results, the Silhouette index, the Davies Bouldin index, and the Calinski Harabasz index can predict the optimal cluster number is more precise than the Sum of Square index.

Abstrak :
Cluster analysis is a mult ivariate analysis technique used to classify objects such that the objects in a cluster are very similar and the objects in different clusters are quite different. This study will discuss the non-hierarchical clustering methods. The methods are C-Means Cluster and Fuzzy C-Means Cluster. These methods are suitable for large data and continuous variables. This study would also present the application of the methods on the case of village grouping according to the underdevelopment status in two regions of level II (Kota Metro and Kabupaten Lampung Timur) in Lampung Province. The unit of observations in this study are 257 villages in Kota Metro (22 villages) and Kabupaten Lampung Timur in Lampung Province obtained from the Village Potential Statistics (Podes - Potensi Desa) 2008. The results show that the optimal cluster in Kota Lampung data is 4, with a minimum value of the Fukuyama-Sugeno validity index is at -45.4649. As for the data of Kabupaten Lampung Timur, theoptimumnumber ofclustersis13,with aminimum value of the Fukuyama-Sugeno validity index is at 196.9629.