Open AccessCLASSIFICATION OF NEWS TYPES BY IMPLEMENTING ENHANCED CONFIX STRIPPING STEMMER
Author(s) -
Muhammad Ichwan Utari,
Henny Medyawati
Publication year - 2020
Publication title -
international journal of engineering technologies and management research
Language(s) - English
Resource type - Journals
ISSN - 2454-1907
DOI - 10.29121/ijetmr.v6.i5.2019.380
Subject(s) - indonesian , computer science , entertainment , world wide web , news media , process (computing) , automation , multimedia , information retrieval , advertising , engineering , political science , business , mechanical engineering , philosophy , linguistics , law , operating system
News has become a community need in the world. Managing a lot of news articles is not easy and takes a long time. Indonesia has various types of media platforms that display news, one of which is an online news portal. Automation systems that are capable of managing and grouping Indonesian language news articles are needed. This study designed and built a web-based application to classify types of Indonesian language news articles by implementing the Enhanced Confix Stripping Stemmer algorithm. The categories used in the system are entertainment, lifestyle, sports, technology, and economics. The data used is secondary data quoted from 2 online news portals in Indonesia. The system development method used is Rapid Application Development. The data used for testing amounts to 30 news. The average results obtained from the system accuracy test are 63%. This shows that the system performance for the classification of news types is good. The number of words in a news article is very influential during the classification process.