Pengelompokan Artikel Berbahasa Indonesia Berdasarkan Struktur Laten Menggunakan Pendekatan Self Organizing Map
Abstract: Document grouping is
a necessity among a large number of articles published on internet. Several
attempts have been done to improve this grouping process, while majority of the
efforts are based on word appearance. In order to improve its quality, the
grouping of documents need to be based on topic similarity between documents,
instead of the frequency of word appearance. The topic similarity could be
known from its latency, since the similarity of the word interpretation are
often used in the same context. In the unsupervised learning process, SOM is
often used, in which this approach simplifies the mapping of multi-dimension
data. This research result shows that implementation of the latent structure
decreases characteristic dimension by 32% of the word appearance, hence makes
this approach more time efficient than others. The latent structure, however,
when implemented on SOM Algorithm, is capable to obtain good quality result
compared to word appearance frequency approach. It is then proven by 5%
precision improvement, recall improvement of 3%, and another 4% from F-measure.
While the achievement is not quite significant, the quality improvement is able
to put the dominance of grouping process, compared to the original
classification defined by the content provider.
Penulis: Akhmad Zaini, M. Aziz
Muslim, Wijono
Kode Jurnal: jptlisetrodd170487
