文章基本信息

标题：Comparison of stemming algorithms and its effect on Indonesian text processing
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作者：Afian Syafaadi Rizki ; Aris Tjahyanto ; Rahmat Trialih 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：1
页码：95-102
DOI：10.12928/telkomnika.v17i1.10183
出版社：Universitas Ahmad Dahlan
摘要：Stemming is one of the stages performed on the process of extracting information from the text. Stemming is a process of converting words into their roots. There is an indication that the most accurate stemmer algorithm is not the only way to achieve the best performance in information retrieval (IR). In this study, seven Indonesian stemmer algorithms and an English stemmer algorithm are compared, they are Nazief, Arifin, Fadillah, Asian, Enhanched confix stripping (ECS), Arifiyanti and Porter. The data used are 2,734 tweets collected from the official twitter account of PLN. First, the aims are to analyze the correlation between stemmer accuracy and information retrieval performance in Indonesian text language. Second, is to identify the best algorithm for Indonesian text processing purpose. This research also proposed improved algorithm for stemming Indonesian text. The result shows that correlation found in the previous research does not occur for the Indonesian language. The result also shows that the proposed algorithm was the best for Indonesian text processing purpose with weighted scoring value of 0.648.
其他摘要：Stemming is one of the stages performed on the process of extracting information from the text. Stemming is a process of converting words into their roots. There is an indication that the most accurate stemmer algorithm is not the only way to achieve the best performance in information retrieval (IR). In this study, seven Indonesian stemmer algorithms and an English stemmer algorithm are compared, they are Nazief, Arifin, Fadillah, Asian, Enhanched confix stripping (ECS), Arifiyanti and Porter. The data used are 2,734 tweets collected from the official twitter account of PLN. First, the aims are to analyze the correlation between stemmer accuracy and information retrieval performance in Indonesian text language. Second, is to identify the best algorithm for Indonesian text processing purpose. This research also proposed improved algorithm for stemming Indonesian text. The result shows that correlation found in the previous research does not occur for the Indonesian language. The result also shows that the proposed algorithm was the best for Indonesian text processing purpose with weighted scoring value of 0.648.
关键词：confix stripping stemmer;Indonesian stemmer;information retrieval;text clustering