期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：7
页码：2052-2061
出版社：Journal of Theoretical and Applied
摘要：The number of documents on the Internet has increased exponentially. Every day, users upload various documents to the Internet. This raises a problem, how to find content documents that are relevant to user queries. Information Retrieval (IR) become a useful thing to retrieve documents. However, IR still uses a keyword-based approach to content search that has limitations in displaying the meaning of the content. Often, keywords are used mismatch and miss concept with a collection of documents. As a result, IR displays documents that are not relevant to the context of the information needed. To overcome these limitations, this study has applied Ontology-based IR. The dataset used in the study is 100 learning documents in the field of Informatics which include lecture material, practicum modules, lecturer presentations, proceedings articles, and journals. IR performance evaluation is done by comparing ontology-based IR with classical IR (keyword based). We evaluate IR performance by executing ten queries for testing. Documents that retrieves by query execution are calculated for performance by using Precision, Recall, and F-Measure evaluation metrics. Based on IR performance evaluation, obtained average recall, precision and f-measure values for ontology-based IR of 88.11%, 83.38%, and 85.49%. Meanwhile, IR classics obtained average recall, precision, and f -measure 78.70%, 70.96%, and 74.47%. Based on the values of Recall, Precision, and F-Measure, it can be concluded that the use of ontology can improve relevance document.
