E-Wallet Sentiment Analysis Using Na¨ıve Bayes and Support Vector Machine Algorithm

E-Wallet Sentiment Analysis Using Na¨ıve Bayes and Support Vector Machine Algorithm

• By Kristiyanti, Dinar Ajeng; Putri, Dwi Andini; Indrayuni, Elly; Nurhadi, Acmad; Umam, Akhmad Hairul;
• Seminar Internasional Bereputasi
• 449

Nowadays most of consumers in urban areas are accustomed to using digital wallets. The habit of transaction in cashless has been widely applied to the transportation system, restaurants and shops in the mall or supermarket. Apart of the ease of conducting transactions, various promotions in the form of points and cashback offered from various digital wallet application developers or e-wallets have become very attractive to users. One of the most widely used e-wallets by the public is OVO and DANA. This phenomena encourages researchers to do a research and make it as an object of study due to both are widely discussed by various groups, especially in the capital of Jakarta lately. As it is used, many customers write product and service reviews based on their experience on the Google Play store. Sentiment analysis is a technique that can find the right solution in creating a system that can automatically analyse these reviews and extract information that is most relevant to users. Researchers collected OVO and DANA review data on the Google Play store with a total of 2000 datasets. In this study, researchers compared the two algorithms namely Na¨ıve Bayes and Support Vector Machine (SVM). The stages carried out in this study are data collection, initial data processing, modelling with the chosen method, experimental & model testing as well as evaluation and validation of result. Evaluation is carried out using 10 Fold Cross Validation. The result showed that OVO is the most popular e-wallet application by the public with an accuracy measurement using the Confusion Matrix reaching 91.00% for the SVM algorithm. The ROC curve showed the best AUC result of 0.986 (Excellent Classification). 

• Katakunci
• URL https://iopscience.iop.org/article/10.1088/1742-6596/1641/1/012079
• Penerbit ,

REFERENSI

[1] Aaputra S A Didi Rosiyadi Windu Gata and Syepry Maulana Husain, 2019 Sentiment Analysis Analisis Sentimen E-Wallet Pada Google Play Menggunakan Algoritma Naive Bayes Berbasis Particle Swarm Optimization J. RESTI (Rekayasa Sist. dan Teknol. Informasi) 3, 3 p. 377–382.

[2] Budiansyah A, 2020, GoPay & OVO Cs Kian Populer, Transaksi Tembus Rp 145 T, CNBC Indonesia, February. p. Online.

[3] Vena, 2020, 5 Dompet Digital Paling Populer di Indonesia. Fyine - PT. Korina Prima Sinergi, Jakarta.

[4] Devita V D, 2019, Siapa Aplikasi E-wallet dengan Pengguna Terbanyak di Indonesia?, iPrice, August.

[5] Fira, 2020, 5 Aplikasi EMoney Terbaik di Indonesia, Kamu Suka yang Mana? 

[6] Brody S, 2010 An Unsupervised AspectSentiment Model for Online Reviews Slide Comput. Linguist. June p. 804–812.

[7] Azam N and Yao J, 2012 Comparison of term frequency and document frequency based feature selection metrics in text categorization Expert Syst. Appl. 39, 5 p. 4760–4768.

[8] Wahyudi M and Kristiyanti D A, 2016 Sentiment analysis of smartphone product review using support vector machine algorithm-based particle swarm optimization J. Theor. Appl. Inf. Technol. 91, 1.

[9] Kristiyanti D A, 2015 Analisis sentimen review produk kosmetik melalui komparasi feature selection Konf. Nas. ilmu Pengetah. dan Teknol. 2, 2 p. 74–81.

[10] Kristiyanti D A, 2015 Analisis Sentimen Review Produk Kosmetik Menggunakan Algoritma Support Vector Machine Dan Particle Swarm Optimization Sebagai Seleksi Fitur Semin. Nas. Inov. Tren 2015 “Peluang dan Tantangan Indones. Dalam Menyikapi Afta 2015” p. 134–141.

[11] Kristiyanti D A Umam A H Wahyudi M Amin R and Marlinda L, 2018 Comparison of SVM Na¨ıve Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Based on Public Opinion on Twitter in 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018 p. 1–6.

[12] Zhang Z Ye Q Zhang Z and Li Y, 2011 Sentiment classification of Internet restaurant reviews written in Cantonese Expert Syst. Appl. 38, 6 p. 7674–7682.

[13] Ye Q Zhang Z and Law R, 2009 Sentiment classification of online reviews to travel destinations by supervised machine learning approaches Expert Syst. Appl. 36, 3 PART 2 p. 6527–6535.

[14] Kristiyanti D A Normah and Umam A H, 2019 Prediction of Indonesia presidential election results for the 2019-2024 period using twitter sentiment analysis Proc. 2019 5th Int. Conf. New Media Stud. CONMEDIA 2019 p. 36–42.

[15] Kristiyanti D A and Normah N, 2019 Optimising the Particle Swam Optimazion Usage for Predicting Indonesia Presidential Election Result Period 2019-2024 SinkrOn 4, 1 p. 32.

[16] S. S and K.V. P, 2020 Sentiment analysis of malayalam tweets using machine learning techniques ICT Express xxxx p. 2–7.

[17] Preety and Dahiya S, 2017 Sentiment Analysis using Na¨ıve bayes Algorithm Int. J. Comput. Sci. Eng. 5, 7 p. 75–77.

[18] Nayak A, 2016 Comparative study of Na¨ıve Bayes , Support Vector Machine and Random Forest Classifiers in Sentiment Analysis of Twitter feeds Int. J. Adv. Stud. Comput. Sci. Eng. 5, 1 p. 14–17.

[19] Chen J Huang H Tian S and Qu Y, 2009 Feature selection for text classification with Na¨ıve Bayes Expert Syst. Appl. 36, 3 PART 1 p. 5432–5435.

[20] Uysal A K and Gunal S, 2012 A novel probabilistic feature selection method for text classification KnowledgeBased Syst. 36 p. 226–235.

[21] Basari A S H Hussin B Ananta I G P and Zeniarja J, 2013 Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization Procedia Eng. 53, December p. 453–462.

[22] Maindola P Singhal N and Dubey A D, 2018 Sentiment Analysis of Digital Wallets and UPI Systems in India Post Demonetization Using IBM Watson 2018 Int. Conf. Comput. Commun. Informatics, ICCCI 2018 p. 1–6