Research Article
Clustering Sports News in Indonesian Using Modified K-Medoid Method
@INPROCEEDINGS{10.4108/eai.24-10-2018.2280569, author={Yoga Dwitya Pramudita and Sigit Susanto Putro and Eka Malasari Rochman and Ach. Yasir Rofiqi and Achmad Jauhari and Ika Oktavia Suzanti and Aeri Rachmad}, title={Clustering Sports News in Indonesian Using Modified K-Medoid Method}, proceedings={The 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus}, publisher={EAI}, proceedings_a={ICCSET}, year={2018}, month={11}, keywords={sport news speak clustering k-medoid modified k-medoid}, doi={10.4108/eai.24-10-2018.2280569} }- Yoga Dwitya Pramudita
Sigit Susanto Putro
Eka Malasari Rochman
Ach. Yasir Rofiqi
Achmad Jauhari
Ika Oktavia Suzanti
Aeri Rachmad
Year: 2018
Clustering Sports News in Indonesian Using Modified K-Medoid Method
ICCSET
EAI
DOI: 10.4108/eai.24-10-2018.2280569
Abstract
Sports news is a topic with high internet access rankings in Indonesia. The number of documents that must be managed continues to increase. In this research, news documents are grouped using the k-Medoid method. Medoid amount is adjusted to the number of news topics. In the k-Medoid method the cluster initialization process greatly influences the cluster results. Medoid is taken randomly according to the number of clusters desired. If the randomization results in a high degree of similarity, the resulting cluster is not optimal. Modifications are made to the initialization process so that the cluster has a low level of similarity. The test results showed accuracy reached 0.584 with five clusters. Modification of the k-Medoid method by adding the cosine similarity method can increase the average accuracy value from 0.44 to 0.54.