期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2015
卷号：13
期号：4
页码：1456-1465
DOI：10.12928/telkomnika.v13i4.2300
语种：English
出版社：Universitas Ahmad Dahlan
摘要：Non-functional requirements is one of the important factors that play a role in the success of software development that is often overlooked by developers, so it cause adverse effects. In order to obtain the non-functional requirements, it requires an identification automation system of non-functional requirements. This research proposes an automation system of identification of non-functional requirements from the requirement sentence-based classification algorithms of FSKNN with the addition of semantic factors such as the term development by hipernim and measurement of semantic relatedness between the term and every category of quality aspects based ISO / IEC 9126. In the test, the dataset is 1342 sentences from six different datasets. The result of this research is that the Semantic-FSKNN method can reduce the value of hamming loss or error rate by 21.9%, and also raise the value of accuracy by 43.7%, and also the precision value amounted to 73.9% compared to FSKNN method without the addition of semantic factors in it.
其他摘要：Non-functional requirements is one of the important factors that play a role in the success of software development that is often overlooked by developers, so it cause adverse effects. In order to obtain the non-functional requirements, it requires an identification automation system of non-functional requirements. This research proposes an automation system of identification of non-functional requirements from the requirement sentence-based classification algorithms of FSKNN with the addition of semantic factors such as the term development by hipernim and measurement of semantic relatedness between the term and every category of quality aspects based ISO / IEC 9126. In the test, the dataset is 1342 sentences from six different datasets. The result of this research is that the Semantic-FSKNN method can reduce the value of hamming loss or error rate by 21.9%, and also raise the value of accuracy by 43.7%, and also the precision value amounted to 73.9% compared to FSKNN method without the addition of semantic factors in it.
