Peringkasan Teks Bahasa Indonesia dengan Pemilihan Fitur C4.5 dan Klasifikasi Naive Bayes

Abstract

This research summarized Indonesian text documents using naive bayes (NB) classification method. Segmentation of the documents into sentences and feature computation are the initial stages of training the system to determine which sentences are classified as summary. The classification used 11 features (f1-f11). The features are selected using C4.5 decision tree to determine the features that affect the summary, reduce the number of features and speed up the summarization. The accuracy of summarization using 10 features (f1-f10) was 34.63%, 37.96%, and 28.14% for compression rate (CR) of 10%, 20%, and 30%, respectively. Adding f11 and C4.5 produced an accuracy of 52.45%, 51.49% and 51.35% for CR 10%, 20%, and 30%, respectively. Text summarization using NB classification, C4.5 feature selection, and additional f11 feature produced better accuracy and faster summarization.