Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier - Trihanto, Wandha Budhi; Arifudin, Riza; Muslim, Much Aziz (Universitas Negeri Semarang, 2017)
The journal is known as one of the relevant serial literature that can support a researcher in doing his research. In it’s development journal has two formats that can be accessed by library users namely: printed format and digital format. Then from the number of published journals, not accompanied by the growing amount of information and knowledge that can be retrieved from these documents. The TF-IDF method is one of the fastest and most efficient text mining methods to extract useful words as the value of information from a document. This method combines two concepts of weight calculation that is the frequency of word appearance on a particular document and the inverse frequency of documents containing the word. Furthermore, data analysis of journal title is done by Naïve Bayes Classifier method. The purpose of the research is to build a website-based information retrieval system that can help to classify and define trends from Indonesian journal titles. This research produces a system that can be used to classify journal titles in Indonesian language, with system accuracy in determining the classification of 90,6% and 9,4% error rate. The highest percentage result that became the trend of title classification was decision support system category which was 24.7%.