Sistem Klasifikasi Berita Menggunakan Metode Text Mining pada Website Pusat Kegiatan Belajar Masyarakat

Abstract

The use of information systems via websites is not uncommon in this current era. This is because the importance of information quality can affect trust, credibility of an organization, and often used as a promotional media. However, the problem arises when there are increasing numbers of news to be informed, which becomes a problem for web managers. Therefore, a faster method and proper news classification system is needed to avoid future problems. Thus, this research uses the text mining method and pure Term Frequency algorithm to calculate the weight of each word, in order to determine which category the news belongs to automatically. To simplify the system design process, Unified Modeling Language (UML) and PIECES analysis are used to analyze the impact factors that will arise later. Based on the results of the classification system testing, it has been able to provide solutions to categorize information in PKBM, even though there are many news articles with different categories.