Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Fachrul Rozy Saputra Rangkuti
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analisis Sentimen Opini Film Menggunakan Metode Naive Bayes dengan Ensemble Feature dan Seleksi Fitur Pearson Correlation Coefficient Fachrul Rozy Saputra Rangkuti; Mochammad Ali Fauzi; Yuita Arum Sari; Eka Dewi Lukmana Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (524.62 KB)

Abstract

Microblogging has become the media information that is very popular among internet users. Therefore, the microblogging became a source of rich data for opinions and reviews especially on movie reviews. We proposed, sentiment analysis on movie review using ensemble features and Bag of Words and selection Features Pearson's Correlation to reduce the dimension of the feature and get the optimal feature combinations. Use the feature selection is done to improve the performance of the classification, reducing the dimension of the feature and get the optimal feature combinations. The process of classification using several models of Naive Bayes i.e. Bernoulli Naive Bayes for binary data , Gaussian Naive Bayes for continuous data and Multinomial Naive Bayes for numeric data. The results of this study indicate that by using the non-standard word on tweet evaluation results obtained accuracy 82%, precision 86%, recall 79.62% and f-measure 82.69% using Feature Selection 20%. Then after using manual standardization of word the evaluation results on the accuracy increased by 8% and then the accuracy becomes 90%, precision 92%, recall 88.46% and f-measure 90.19% using 85% feature selection. Based on these results it can be concluded that by using the standardization of word can improve the performance of classification and feature selection Pearson's provide optimal feature combinations and reducing the total number of dimensions feature.
Co-Authors Eka Dewi Lukmana Sari M. Ali Fauzi Yuita Arum Sari