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Journal : Indonesian Journal of Computer Science

Incremental News Mining Using Evolving Clustering with Functional Operators Amalia Wirdatul Hidayah; Ali Ridho Barakbah; Iwan Syarif
Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): Indonesian Journal of Computer Science Volume 12. No. 2 (2023)
Publisher : STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i2.3197

Abstract

Online media publish journalistic products, one of which is news online (online news). This is in line with the findings of the Ministry of Communication and Informatics (Kemkominfo), that in 2018 there were 43,000 online media in Indonesia. On generally in getting actual news, humans tend to read the news on online media one by one. The activity is not effective because of the news that produced by online media have the same information with each other news. In this study, we propose an innovative solution to this issue by developing a news mining system that employs clustering based on an evolving system. This system has the potential to improve the effectiveness of news retrieval by grouping similar news together and identifying key information trends, ultimately enhancing the ability of individuals to obtain actual news. Based on research observations, the performance of news clustering using an evolving clustering system with functional operators is quite good, as evidenced by an accuracy of 83%.
Semantic Information Search with Automatic Ontology Creation in Regulations National Standards for Higher Education in Indonesia Nadila Wirdatul Hidayah; Ali Ridho Barakbah; Iwan Syarif
Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): Indonesian Journal of Computer Science Volume 12. No. 3 (2023)
Publisher : STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3207

Abstract

In Indonesia, there are around ten types of legal products that contain higher education regulations. With a large number of articles, more effort is needed when users search for links between one article and another. Based on these problems, it is necessary to have an automatic article representation search system using an automatic ontology. Ontology refers to the hierarchical structure of entities and their relationships. In this paper, the results of the development of an information retrieval system with an automated ontology will be explained. This system describes a process begins with receiving input of higher education regulatory files which are used as data samples Permendikbud No 3 of 2020. Then split the data into articles, paragraphs and contents which are then formed ontologies by building 3 detection functions (Definitive Creation, Compound Creation, and Reference Detection). System output has an accuracy of search results reaching an accuracy of 92.5%.