Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016
Introduction. The Australian Embassy in Jakarta is storing a wide array of media release document. Analyzing particular and vital patterns of the documents collection is imperative as it will result in new insights and knowledge of significant topic groups of the documents. Methodology. K-Means was...
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2019-11-01
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| Series: | Berkala Ilmu Perpustakaan dan Informasi |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/bip/article/view/36451 |
| _version_ | 1828443340768542720 |
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| author | Wishnu Hardi Wisnu Ananta Kusuma Sulistyo Basuki |
| author_facet | Wishnu Hardi Wisnu Ananta Kusuma Sulistyo Basuki |
| author_sort | Wishnu Hardi |
| collection | DOAJ |
| description | Introduction. The Australian Embassy in Jakarta is storing a wide array of media release document. Analyzing particular and vital patterns of the documents collection is imperative as it will result in new insights and knowledge of significant topic groups of the documents.
Methodology. K-Means was used algorithm as a non-hierarchical clustering method which partitioning data objects into clusters. The method works through minimizing data variation within cluster and maximizing data variation between clusters.
Data Analysis. Of the documents issued between 2006 and 2016, 839 documents were examined in order to determine term frequencies and to generate clusters. Evaluation was conducted by nominating an expert to validate the cluster result.
Results and discussions. The result showed that there were 57 meaningful terms grouped into 3 clusters. “People to people links”, “economic cooperation”, and “human development” were chosen to represent topics of the Australian Embassy Jakarta media releases from 2006 to 2016.
Conclusions. Text mining can be used to cluster topic groups of documents. It provides a more systematic clustering process as the text analysis is conducted through a number of stages with specifically set parameters. |
| first_indexed | 2024-12-10T21:27:54Z |
| format | Article |
| id | doaj.art-bdfe60eb5c9e469a82dbfc41a7bd9d71 |
| institution | Directory Open Access Journal |
| issn | 1693-7740 2477-0361 |
| language | English |
| last_indexed | 2024-12-10T21:27:54Z |
| publishDate | 2019-11-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | Berkala Ilmu Perpustakaan dan Informasi |
| spelling | doaj.art-bdfe60eb5c9e469a82dbfc41a7bd9d712022-12-22T01:32:56ZengUniversitas Gadjah MadaBerkala Ilmu Perpustakaan dan Informasi1693-77402477-03612019-11-0115222623810.22146/bip.3645124666Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016Wishnu Hardi0Wisnu Ananta Kusuma1Sulistyo Basuki2National Library of AustraliaInstitut Pertanian BogorUniversitas IndonesiaIntroduction. The Australian Embassy in Jakarta is storing a wide array of media release document. Analyzing particular and vital patterns of the documents collection is imperative as it will result in new insights and knowledge of significant topic groups of the documents. Methodology. K-Means was used algorithm as a non-hierarchical clustering method which partitioning data objects into clusters. The method works through minimizing data variation within cluster and maximizing data variation between clusters. Data Analysis. Of the documents issued between 2006 and 2016, 839 documents were examined in order to determine term frequencies and to generate clusters. Evaluation was conducted by nominating an expert to validate the cluster result. Results and discussions. The result showed that there were 57 meaningful terms grouped into 3 clusters. “People to people links”, “economic cooperation”, and “human development” were chosen to represent topics of the Australian Embassy Jakarta media releases from 2006 to 2016. Conclusions. Text mining can be used to cluster topic groups of documents. It provides a more systematic clustering process as the text analysis is conducted through a number of stages with specifically set parameters.https://jurnal.ugm.ac.id/bip/article/view/36451text miningdocument clusteringk-means algorithm, cosine similarity |
| spellingShingle | Wishnu Hardi Wisnu Ananta Kusuma Sulistyo Basuki Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016 Berkala Ilmu Perpustakaan dan Informasi text mining document clustering k-means algorithm, cosine similarity |
| title | Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016 |
| title_full | Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016 |
| title_fullStr | Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016 |
| title_full_unstemmed | Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016 |
| title_short | Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016 |
| title_sort | clustering topic groups of documents using k means algorithm australian embassy jakarta media releases 2006 2016 |
| topic | text mining document clustering k-means algorithm, cosine similarity |
| url | https://jurnal.ugm.ac.id/bip/article/view/36451 |
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