Generating Javanese Stopwords List using K-means Clustering Algorithm

Aji Prasetya Wibawa (Electrical Engineering Department, Universitas Negeri Malang Jl Semarang 5, Malang, East Java 65145, Indonesia)
Hidayah Kariima Fithri (Electrical Engineering Department, Universitas Negeri Malang Jl Semarang 5, Malang, East Java 65145, Indonesia)
Ilham Ari Elbaith Zaeni (Electrical Engineering Department, Universitas Negeri Malang Jl Semarang 5, Malang, East Java 65145, Indonesia)
Andrew Nafalski (UniSA Education Futures, School of Engineering, University of South Australia SCT2-39 Mawson Lakes Campus, Adelaide, South Australia 5095, Australia)

Knowledge Engineering and Data Science
Vol 3, No 2 (2020)

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.