| dc.description.abstract | K-Means is a clustering algorithm that groups data based on similarities between
data. Some of the problems that arise from this algorithm are when determining
the center point of the cluster randomly. This will certainly affect the final result of a
clustering process. To anticipate the poor accuracy value, a process is needed to
determine the initial centroid in the initialization process. The second problem is
when calculating the Euclidean distance on the distance between data. However,
this method only gives the same impact on each data attribute. From some of
these problems, this study proposes the Rank Order Centroid(ROC) method for
initializing the cluster center point and using the Braycurtis distance method to
calculate the distance between data. With the experiment K=2 to K=10, the results
obtained in this study are the proposed method obtains an iteration reduction
of 6.6% on the Student Performance Exams dataset and 19.3% on the Body
Fat Prediction dataset. However, there was an increase in iterations on the
Heart Failure dataset by 24.2%. In testing the cluster results using the
Silhouette Coefficient, this method shows an increase in the evaluation value of
5.9% in the Student Performance Exams dataset. However, the evaluation value
decreased by 8.3% in the Body Fat Prediction dataset and 3.3% in the Heart Failure
dataset. | en_US |
