Open AccessRecommender System for Topic Articles Based on Forum Trending using Multilayer Perceptron
Author(s) -
Sri Hesti Mahanani,
Tuga Mauritsius
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8194.038620
Subject(s) - computer science , variety (cybernetics) , world wide web , sorting , information retrieval , data science , recommender system , artificial intelligence , programming language
SehatQ is a portal and application that helps manage personal and family health. One of SehatQ's services is providing information and directories in the form of articles. To improve relations with web visitors, SehatQ also provides services in the form of discussion forums. The forum actually contains a variety of topics and changes very quickly over time, so to identify a topic from a collection of forums is very difficult and time-consuming if done manually by humans. But unfortunately the SehatQ editorial team has limited time and human resources in sorting out information sourced from the SehatQ forum to draw conclusions as a topic in the article. This research will offer a solution in analyzing Topic modeling using text mining with the Multilayer perceptron algorithm to provide trending information on the topics most frequently discussed at the forum at a certain time.