K-Means and K-Medoids for Indonesian Text Summarization

Senin, 26 Oktober 2020 - 08:35
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K-Means and K-Medoids for Indonesian Text Summarization
 
 
Ketua : KEN KINANTI PURNAMASARI S.Kom, M.T
Faculty of Engineering and Computer Science
Universitas Komputer Indonesia
Indonesia

Email : ken.kinanti@email.unikom.ac.id
 

Abstract. The purpose of this study is to build automated summation tools, especially in
grouping methods such as K-Means and K-Medoids. Finding the best method between the two
algorithms, this study focuses on comparing the two methods to summarize thesis report
documents. This system is divided into Filtering, Tokenization, TF-IDF, Cosine Similarity, and
Clustering. Based on 50 test documents, the average accuracy rate is 51.16% for K-Means and
63.35% for K-Medoids. K-Means has a smaller accuracy value than K-Medoids. The accuracy
of the resulting K-Means also depends on the size and center of the initial cluster chosen. So, as
the next stage of development, research needs to be done that compares the results of the
combination of initial size and center cluster values for K-Means and continue with several other
classifications.