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All Journal International Journal of Electrical and Computer Engineering IT JOURNAL RESEARCH AND DEVELOPMENT Dinamisia: Jurnal Pengabdian Kepada Masyarakat INTECOMS: Journal of Information Technology and Computer Science EDUKATIF : JURNAL ILMU PENDIDIKAN Jurnal Linguistik Komputasional jurnal teknik informatika dan sistem informasi Journal of Data Science and Its Applications Community Education Engagement Journal CONSEN: Indonesian Journal of Community Services and Engagement
Arbi Haza Nasution, Arbi Haza
Universitas Islam Riau
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Mathematics Physics Social Sciences Other

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Journal : Journal of Data Science and Its Applications

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Visualizing Language Lexical Similarity Clusters: A Case Study of Indonesian Ethnic Languages Arbi Haza Nasution; Yohei Murakami
Journal of Data Science and Its Applications Vol 2 No 2 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2019.2.23

Abstract

Language similarity clusters are useful for computational linguistic researches that rely on language similarity or cognate recognition. The existing language similarity clustering approach which utilizes hierarchical clustering and k-means clustering has difficulty in creating clusters with a middle range of language similarity. Moreover, it lacks an interactive visualization that user can explore. To address these issues, we formalize a graph-based approach of creating and visualizing language lexical similarity clusters by utilizing ASJP database to generate the language similarity matrix, then formalize the data as an undirected graph. To create the clusters, we apply a connected components algorithm with a threshold of language similarity range. Our interactive online tool allows a user to dynamically create new clusters by changing the threshold of language similarity range and explore the data based on language similarity range and number of speakers. We provide an implementation example of our approach to 119 Indonesian ethnic languages. The experiment result shows that for the case of low system execution burden, the system performance was quite stable. For the case of high system execution burden, despite the fluctuated performance, the response times were still below 25 seconds, which is considered acceptable.
Co-Authors Andrian, Dedek Anggi Hanafiah Arif Lukman Hakim Evizariza Evizariza Febri Loska Hafiza Oktasia Nasution Husnul Kausarian Jerika Mardafora Laksono Trisnantoro Lukman Nul Hakim M. Rizki Fadhilah Nofriyandi Nofriyandi Rafi Muhammad Rizky Wandri Salhazan Nasution Salhazan Nasution Salman Saragih Siti Nurhalimah Syafhendry Syafhendry Syafrinaldi Syafrinaldi Toru Ishida Winda Monika Yohei Murakami Yoze Rizki Yudhi Arta Zafrullah