Knowledge Engineering and Data Science


Generating Javanese Stopwords List using K-means Clustering Algorithm

Wibawa, Aji Prasetya (Unknown)
Fithri, Hidayah Kariima (Unknown)
Zaeni, Ilham Ari Elbaith (Unknown)
Nafalski, Andrew (Unknown)



Article Info

Publish Date
01 Dec 2020

Abstract

Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created by a Javanese linguist. The accuracy of the proposed method is 78.28% (K=7). The result shows that the generation of Javanese stopword lists using a clustering method is reliable.

Copyrights © 2020






Journal Info

Abbrev

publication:keds

Publisher

Subject

Computer Science & IT Engineering

Description

The journal welcomes experimental and theoretical findings on data science and knowledge engineering along with their applications to real-life ...