Ethno-informatics for anthroponymy classification in Sumedang District using data mining
Journal of Physics: Conference Series(2021)
Abstract
Abstract Ethnoscience is used to look at culture from a scientific perspective, helping to understand how to develop different forms of knowledge and beliefs. One of the fields studied in Ethnoscience is Ethno-informatics, which is the application of informatics in culture. The methodology used is the process of knowledge discovery in databases in data mining, the process of automatically extracting knowledge from large databases, to get interesting patterns so that knowledge can be obtained. The application of anthroponymy by exploring the population database in Sumedang district, the results of the 2019 census from the Sumedang District Population Office. Then the anthroponymy application was developed in Sumedang district. The results of data processing show that the top ten names of people who are most used or most favorite in Sumedang district are: Muhammad, Muhamad, Asep, Siti, Agus, Dede, Ade, Ai, Tati, Dadang. Muhammad’s name has been a favorite since 2000 until now 2020. Meanwhile, the top ten favorite names that have just appeared in the last ten years (2010-2020) are sorted based on ranking including; Naura, Arsyla, Keyla, Raffa, Rafka, Aqila, Zahra, Keusha and Alesha. In general, the Sundanese name in Sumedang district is still widely used, but generally it is relatively down-to-earth, replaced by new names as an uptake of other cultural names. Changes in self-naming in Sumedang regency in particular, generally self-names in Sundanese culture in West Java, were caused by the rapid development of information and communication technology. This research is expected to provide input for the community, especially for the younger generation as a guide in their daily life. It takes a significant effort from the government to maintain it, so that local culture and wisdom, especially the naming of people in Sumedang district, generally Sundanese culture can be well preserved.
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Key words
anthroponymy classification,sumedang district,data mining,ethno-informatics
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