Algorithm Of K-Medoids Analyzes Personality Types Based On Holland Theory
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AUTHOR(S)
Haryadi, Ganefri, Fahmi Rizal, Yuyun Yusnida Lase, Yulia Fatmi, B.H.Hayadi, M.Ropianto
KEYWORDS
Algorithm of K-Medoids, Personality Type, Holland Theory
ABSTRACT
the difficulty of identifying someone's personality type and wants to prove the Algorithm of K-Medoids in data mining that is used to do quite a lot of data clustering in determining someone's personality type. This algorithm is also known as Partitioning Around Medoids (PAM), which is a variant of the K-Means method. K-Medoids Clustering exists to overcome the weaknesses of K-Means Clustering. K-Medoids uses the partition clustering method to cluster n-objects into a number of k-clusters. This algorithm uses objects in a collection of objects that represent a cluster. The objects that represent a cluster are called Medoids. Clusters are built by calculating the closeness that is owned by between medoids and non-medoids objects. This study uses the algorithm of K-Medoids in determining personality types based on Holland's theory in the Realistic Type, The Investigative Type, the Artistic Type, the Social Type, the Entrepreneur Type, and Routine Type (Conventional Type). Sample data used in this study were 50 (fifty) students obtained from the results of tests conducted. Data samples were clustered into 6 (six) clusters. From the final results of calculations performed that the level of accuracy of the data in conducting clustering is 68% based on the results of the validation of personality tests conducted on 50 (fifty) students. So from the results of this study indicate that the algorithm of K-Medoids can predict student's personality types for future careers according to their personality.
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