Identify the Relevant Pages of Book to be Indexed Using Naive Bayes Classification Method

Abstract

The back-of-book index is a component that is often found in non-fiction books. The back-of-book index contains important terms and page numbers where the terms appear in the book, it is useful for helping readers find certain term directly, without having to search on every page. Usually, the back-of-book index is compiled by the author or a professional indexer, although currently there are several automatic indexing applications available. To determine a term on a page worth to be indexed requires knowledge and expertise of the author whom better understand the book’s context. Hence, generating a good back-of-book index is a task that requires great effort, knowledge and cost. Therefore, the back-of-book index is very possible containing indexed terms that refer to irrelevant page numbers due to human error or the weakness of the indexing application. This study aims to identify the relevance of the pages to be indexed in the back-of-book index, using Naive Bayes Classification. The testing result shows that the approach with Naive Bayes Classification produces an average precision value of 74.02 percent and 100 percent for the recall value. The average precision value that more than 50 percent indicates that the naive bayes classification approach is capable to identify the relevant and irrelevant pages.