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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Dian Palupi Rini
Sriwijaya University, Palembang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Sentimen E-Wallet di Twitter Menggunakan Support Vector Machine dan Recursive Feature Elimination Elza Fitriana Saraswita; Dian Palupi Rini; Abdiansah Abdiansah
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3118

Abstract

Grouping of positive or negative sentiments in text reviews is increasingly being done automatically for identification. The selection of features in the classification is a problem that is often not solved. Most of the feature selection related to sentiment classification techniques is insurmountable in terms of evaluating significant features that reduce classification performance. Good feature selection technique can improve sentiment classification performance in machine learning approach. First, two sets of customer review data are labeled with sentiment and then retrieved, processed for evaluation. Next, the supports vector machine (svm-rfe) method is created and tested on the dataset. Svm-rfe will be run to measure the importance of the feature by rating the feature iteratively. For sentiment classification, only the top features of the ranking feature sequence will be used. Finally, performance is measured using accuracy, precision, recall, and f1-score. The experimental results show promising performance with an accuracy rate of 81%. This level of reduction is significant in making optimal use of computing resources while maintaining the efficiency of classification performance
Co-Authors Abdiansah, Abdiansah Elza Fitriana Saraswita