Enhancing Focus Topic Findings of Discussion Forum through Corpus Classifier Algorithm

Abstract

In learning management system, a discussion forum, in which the students and lecturers are involved actively as part of the learning method, enriches the context of communication, thereby enhancing the students’ learning and performance. The aim of this paper was to determine the appropriate topics for a discussion forum for learning management systems through enhanced probabilistic latent semantic analysis (PLSA) with the corpus classifier algorithm. In preparing the paper, the methods used were PLSA and the classifying process, which classifies the documents to become a corpus based on the similarity word approach. The similarity word is influenced by the term-frequency of the word in the document. The novel concept in this paper is the corpus classifier algorithm. The experiment was conducted using three approaches to discover the topic, and it used 4,868 distinct words from 234 documents. The documents were contained in three threads subject. The post of the discussion forum is the text document. The performance of the result was measured by the f-measure, which was calculated for each thread subject. The corpus classifier algorithm was used in the second approach, and third approach increased the average f-measure values for the second and third thread subjects by approximately 24 and 17%, respectively.